An introduction to contrastive techniques for representation learning

A look at some of the basic research on contrastive learning

Earlier this year I had to write a technical report on the topic of contrastive learning (link). This post is an effort to create something more digestible and adequate for reading. The goal is to give an introduction to representation learning. This is done in the first section, also discussing its advantages and other related topics such as self-supervised or transfer learning. The second section is dedicated to the techniques for representations learning labelled as “contrastive”. We will go over some of the most important topics: loss functions, data generation techniques, negative examples and applications.

Representations, transfer learning and self-supervised learning

Consider some neural network such as the following:

Neural networks are the composition of transformations, where every intermediate layer applies a parameterized function to the input coming from the previous one. The output of each intermediate layer is a new view or representation of the input data. These representations are nothing but vectors, where the values are features or characteristics of the input. Of course, neural networks are not interpretable, and these vectors do not necessarily hold any meaning for us. The network learns to progressively transform the data into a set of features that is useful to perform the task we are training it for. For example, if we are training for image classification of animals, the network might learn a representation with the number of legs, the shape of the eyes, the color…

At the end of the network, we have a final layer that transforms the last representation into the prediction, such as a regression value or a probability distribution over the different classes in our classification problem.

When we train a network on one dataset, we are not only teaching it how to make predictions, we are teaching it to extract intermediate representations with features that are useful for the task. This is interesting because these representations might be useful for other tasks as well. For example, imagine we want to add a new class of animals in our classification example. Perhaps the network is already capable of getting features to predict that class. We can then modify the final head and freeze the rest of the weights of the network. With fewer parameters to learn, we can train with less data, much faster and with a smaller energy consumption. In fact, if the learned representations are rich enough, we might be able to use the same network for a myriad of different tasks, training only a small head at the end of the network. This is called transfer learning: first pretraining for a pretext task, and then fine-tuning the model (perhaps modifying the head) on the downstream task we are actually interested in solving. The following diagram shows this process.

Learning very general representations: self-supervised learning

Another important and related concept is that of self-supervised learning. This term, although poorly coined in my opinion, can be useful to refer to a particular set of tasks. It refers to supervised tasks where we can generate a training signal automatically, without the need for human labeling. One example is the training procedure of the language model BERT, called Masked Language Modeling (MLM). In this task, some parts of the input text are masked, and the model has to recover them based on the unmasked context. We can then use any available text on the internet and generate training examples by masking parts of it. This allows us to generate massive datasets, and thus train very large neural networks with a nuanced understanding of the distribution of human text. These large networks learn very general representations, that we can then transfer to a great number of tasks. In fact, BERT achieved state-of-the-art performance in several downstream tasks.

Let’s consider another example, this time with images. Images allow us very simple forms of data augmentation: we can crop, rotate, translate, convert to greyscale, modify the brightness… We can train a neural network to produce similar representations for augmented views of the same image, and different representations for views of different images. Again, we can generate examples with any image we find, allowing us to create very large datasets and train massive networks that obtain general representations. As we will see, this is our first example of contrastive learning.

Advantages and disadvantages

Representation learning, together with transfer learning and self-supervised learning, has produced a revolution in the field. Some of the benefits we get from these techniques are:

1. It can greatly improve performance for some tasks. Let’s consider the example of sentiment analysis in text. Textual data is very diverse and nuanced. Even the same word can represent different sentiments depending on the context. A small dataset of examples labeled by humans is unlikely to produce a model with detailed understanding of text. By pretraining a large model on a difficult task with a gigantic set of examples, we get a model with nuanced understanding of text, and this is likely to produce better results on our downstream task as well.

2. Another way to put the previous point is that it alleviates data bottlenecks. If we don’t have enough data to train a decent model for a task, we can try to pretrain on some pretext task and then fine-tune small parts of the model with the supervised data we have.

3. It improves generalization. Let’s consider the sentiment analysis task again. A pretrained model learns representations for many different tokens in a lot of different contexts, since the dataset is very large. In this common feature space, we may find specific features about sentiment, which may be used by the final head we train on supervised data.

   Since the feature space is common to a large number of tokens and contexts, even if they do not appear on the supervised data, the model will be able to extrapolate what they learn.

4. It allows us to use large and powerful models at a fraction of the cost. We find many open source models nowadays, and using them for inference requires much fewer resources than for training.

5. Some model have zero-shot capabilities. This means that we can perform inference on tasks it wasn’t trained for without any need for fine-tuning. We have all seen ChatGPT performing a myriad of different tasks, even though it was only trained to do next-token prediction. Another example is CLIP. This model was trained using contrastive techniques. It encodes text and images into a common feature vector space. If a text describes an image properly, then their representation should be close together in the space. This allows us to perform arbitrary image classification as long as we have textual labels for the classes. We simply pick the class with the textual label that is closest to the image embedding.

Of course, not everything is bright. Training big and general models is very expensive, and only big companies with large resources can afford to do so. We may also find that these models are overkill for some tasks. A smaller and more specialized model can sometimes perform better, specially if the learned representations aren’t very good for the particular task. Finally, although zero-shot capabilities are very interesting, performance tends to be subpar.

Contrastive learning

Roughly speaking, in contrastive learning we try to learn vector representation of the input data by comparing and contrasting examples with each other. For instances that we deem similar in some sense, we want their vector representation to be close according to some distance or similarity function. On the other hand, the vector representation of dissimilar examples should be further apart. Images provide an easy example. We would expect pictures of animals to be close together, and far apart from pictures of buildings. Inside the cluster of pictures of animals, we would also like for pictures of the same animal to be closer than pictures of different animals, and so on.

Basic concepts and notation

Let’s establish a framework and notation for the rest of the text.

• We first consider a set of networks, called encoders, that map the input data to a fixed sized vector representation of dimension $d$. We may have one or multiple encoders, depending on the problem. For example, if we are trying to map both text and images to a common representation space, we need a different encoder for each domain.

  Mathematically, the $k$-th encoder $e_k$ maps the examples in some input space $\mathcal{X}_k$ onto a $d$-dimensional vector space:

  \[\begin{align*} e_k(\cdot;\theta_k): \mathcal{X}_k &\longrightarrow \mathbb{R}^d \\\\ x &\longmapsto e_k(x; \theta_k ) .\end{align*}\]

  Here, $\theta_k$ are the parameters of the neural network that we would like to optimize. In cases where a single encoder is used, we will omit the $k$ subscript.

• Additionally, we can assume without loss of generality that a final head $h_k(\cdot, \eta_k)$ maps these representations to an $m$-dimensional metric space, where the notion of distance or similarity is actually applied between the vectors. The point of adding this final transformation is to separate two tasks: finding a general representation that can be applied to other problems and finding a representation in a metric space where the notion of distance represents some real-world semantic similarity. The former is typically better for transfer learning. If we didn’t need this separation, we may assume that this is the identity function.

• The metric is given by a distance or similarity function $\mathbb{R}^m \times \mathbb{R}^m \to \mathbb{R}$, such as the Euclidean distance $||z_1 - z_2||$ or the cosine similarity function $\frac{\langle z_1, z_2 \rangle}{||z_1|| ||z_2||}$. Similarity functions take larger values for similar examples, and will be denoted by $S$, whereas distance functions, denoted by $S$, are lower bounded by zero and take smaller values for similar instances.

• A loss function $\mathcal{L}$ is built using this similarity notion for a set of encoded examples, penalizing large distances for similar instances, and small distances for dissimilar examples. We will take a closer look into loss functions in the next section.

• Finally, given an example $x \in \mathcal{X}$, we will denote its vector representation in the general feature space by $v=e(x)$, and its embedding in the metric space by $z=h(v)$. Instances that we consider similar to an anchor example $x$ will be called positive examples and be denoted by a plus superscript $x^+$. We define negative examples analogously and denote them by a minus superscript $x^-$. The same superscript notation will also be applied to vectors $v$ and $z$.

This structure is shown graphically for two examples in the following diagram:

Loss functions in contrastive learning

Loss functions are generated from the distance/similarity metric applied to different pairs of examples. It should penalize similar examples being far away and dissimilar examples being close. The latter is as important as the former, otherwise the network could learn the trivial representation of mapping any example to the same vector. Let’s take a look at some common functions.

Pair loss

Let $x \in \mathcal{X}$ be an example and $z$ its embedding in the metric space. The pair loss is defined for pairs of examples, differing for positive and negative ones:

\[\begin{cases} \mathcal{L}(x, x^+) &= D(z, z^+)^2 \\ \mathcal{L}(x, x^-) &= \max (0, \varepsilon - D(z, z^-)^2), \end{cases}\]

where $D$ is some distance function such as the Euclidean distance. By minimizing this expression, the distance to positive samples should be close to zero, and dissimilar instances should be separated by a margin of at least $\varepsilon$. The function is plotted in the following figure.

Plot of the pair loss function for positive (left) and negative (right) examples. Arrows represent gradient direction and norm.

Triplet loss

The triplet loss tries instead to enforce that the distance between the anchor and positive examples is smaller than the distance between the anchor and negative examples, with a difference margin of at least $\varepsilon$:

\[\label{eq:triplet-loss} \mathcal{L}(x, x^+, x^-) = \max(0, D(z,z^+)^2 - D(z,z^-)^2 + \varepsilon) .\]

In this last loss function, a full training example requires now both a similar and a dissimilar instance. However, the number of interactions between different examples is still very limited. The use of many negative examples, specially those that are hard for the model, are crucial for a good and efficient learning process. This makes sense intuitively: there are many more ways in which images can be different than similar.

Lifted Embedding Loss

Increasing the number of interactions between instances at once, Song et al. proposed the Lifted Embedding loss. If $x_1, \cdots, x_n$ is our set of examples, and $P$ and $N$ are the sets of pairs of examples that are considered similar and dissimilar, respectively, then the loss function is given as

\[\label{eq:lifted-structure-loss} \mathcal{L} (N, P) = \frac{1}{2 |P|} \sum_{(i,j) \in P} \max (0, L_{i,j})^2 ,\]

where

\[L_{i,j} = D_{i,j} + \log \left( \sum_{(i,k) \in N} e^{\varepsilon - D_{i,k}} + \sum_{(j,l) \in N} e^{\varepsilon - D_{j,l}} \right) ,\]

and $D_{i,j} = D(z_i, z_j)$. The expression for $L_{i,j}$ is actually used because it is a smooth upper bound for

\[\hat{L}_{i,j} = D_{i,j} + \max \left( \max_{(i,k) \in N} \varepsilon - D_{i,k}, \max_{(j,l) \in N} \varepsilon - D_{j,l} \right) ,\]

so this loss can be interpreted as an adaptation to the triplet loss, trying to mine the hardest negative example of the set for each positive pair, and also squaring the result. As usual in deep learning, instead of using the full set of examples, the loss is approximated with a batch of smaller size.

Binary Noise-Contrastive Estimation (NCE) loss

Let’s now take a probabilistic perspective. Let $X_1$ and $X_2$ be two random variables that take values in our domain of examples $\mathcal{X}$ (e.g. images). Let $Y$ be another random variable that takes the value of $1$ if $X_1$ and $X_2$ are similar according to our concept of similarity, and $0$ otherwise. It follows a Bernoulli distribution, and we can try to approximate its conditional probability mass function with our network as

\[\hat{p} (1 | x_1, x_2) = \sigma ( S(z_1, z_2) ) ,\]

where $\sigma$ is the sigmoid function. If $q^+(\cdot, \cdot)$ and $q^-(\cdot, \cdot)$ are the joint probability density functions of similar and dissimilar instances, respectively, then the Binary Noise-Contrastive Estimation (NCE) loss is given by minimizing the expected negative log-likelihood:

\[\begin{gathered} \label{eq:bin-nce} \mathcal{L}_{Bin-NCE} = -\mathbb{E}_{q^+} \log \hat{p} (1| x_1, x_2) \\ -\mathbb{E}_{q^-} \log (1-\hat{p} (1| x_1, x_2)) , \end{gathered}\]

where the expected value would be approximated by its population mean (e.g. with a single batch). Keeping the previous notation for the sets of positive pairs $P$ and negative pairs $N$, this yields:

\[\begin{gathered} \label{eq:bin-nce-2} \mathcal{L}_{Bin-NCE} = -\frac{1}{|P|} \sum_{(i,j) \in P} \log \sigma(S(z_i, z_j)) \\ -\frac{1}{|N|} \sum_{(i,j) \in N} \log (1-\sigma(S(z_i, z_j))) . \end{gathered}\]

InfoNCE

A more recent loss function is InfoNCE . In their setting, instead of considering a binary classification problem they assume a ranking one. Having fixed an anchor instance $x$, let $x_0^+,x_1^-, \cdots,x_n^-$ be a set of possible similar instances, where only $x_0^+$ is a positive example. The problem becomes one of ranking which example is more likely to be positive. The probability of each of the samples can be approximated by a softmax operation on a similarity score with respect to $x$:

\[\label{eq:info-nce-p} \hat{p} (i|x) = \frac{\exp(S(x, x_i))}{\sum_{j = 0}^{n} \exp(S(x, x_j))} .\]

Minimizing the negative log-likelihood of the true positive yields:

\[\label{eq:info-nce} \mathcal{L}_{InfoNCE} = - \mathbb{E} \log \frac{\exp(S(x, x_0^+))}{\sum_{j = 0}^{n} \exp(S(x, x_j))} .\]

As we will see, it is a common setting to have batches of pairs of similar instances $(x_1,x_1^\prime),\cdots,(x_n,x_n^\prime)$, where instances across pairs are considered to be dissimilar. We can compute a similarity matrix $\mathcal{S} = (S(z_i, z_j^\prime))_{i,j}$, where the main diagonal values should be high and the rest should be low. In this case, one can calculate the InfoNCE across rows or columns. It is also possible to average both options, yielding a symmetric InfoNCE loss.

NT-Xent

A temperature parameter $\tau$ can be included in the sotfmax operation, transforming the softmax probability distribution into

\[P(i|x) = \frac{\exp(S(x, x_i) / \tau)}{\sum_{j = 0}^{n} \exp(S(x, x_j) / \tau)} .\]

A small value of $\tau$ makes the softmax sharper, and small differences between the similarity of positive and negative examples already produce a high likelihood. A large value of $\tau$ forces the difference in similarity to be large. This parameter can be viewed as the margin parameter in previous functions. This modified InfoNCE loss is called NT-Xent (normalized temperature-scaled cross entropy loss).

Generating data for contrastive learning

We have already seen how to create a loss function to train our model, but how do we get appropriate data? In most cases, the question is how to generate pairs of similar examples, as you can often get away with the assumption that examples in different pairs are dissimilar. This is the case when you have a huge domain, such as a large amount of internet images. Getting two similar examples in the same batch by sampling randomly is unlikely. Still, with this assumption we could incur in false negatives, something we will discuss in the next section. Let’s see now some of the ways in which datasets are generated for contrastive learning.

Human supervision

Of course, human annotation is always an option, albeit a very expensive one. Unfortunately, this is sometimes necessary. We previously talked about CLIP, a model that learned representations for text and image, mapping a text that correctly describe an image to an embedding that is close to the embedding of the image. In this case, there is no way around getting someone to create captions for different images (although in the future synthetic data generation with multimodal models might be an option).

One way to reduce the amount of data necessary when human labeling is required is to first pretrain the encoders with some other self-supervised technique. For example, in CLIP, you could train the text and image encoders separately with a denoising task, and then use the supervised data to align the representations of both encoders to a common metric space.

Self-supervision

What if we want to generate large amounts of data from existing sources automatically? As explained earlier, we will generate pairs of similar instances, and assume that images in different pairs are dissimilar. The following are just some examples:

• Data augmentation. In the particular context of contrastive learning, a transformation that does not change the instance semantically can be applied to generate positive examples. To illustrate this, assume that we are contrasting images, and we want to map images based on the concepts inside them. Then if we crop, blur, or saturate the color of an image of a cat, it is still an image of a cat. This way, augmentations of similar or equal instances are also similar, whereas augmentations of dissimilar examples are also dissimilar.

  In the case of images, we find transformations such as rotations, translations, cutouts, cropping and resizing, blurring, applying noise… These were used, for example, in .

  Textual data is a bit more complex. Fang et al. use automatic back-translation, that is, translation to a different language and then back to the source language, to generate pairs of different but semantically equivalent segments of text. Many other techniques can be used: changing characters, masking words, adding noise, changing words for synonyms…

  Careful exploration of data augmentation techniques can be very important. For example, Chen et al. found that the training of SimCLR was very sensitive to the choice of data augmentation techniques.

• Multi-sensor. When multiple inputs are captured simultaneously, we can use all inputs from corresponding times. For example, capturing the same image from different angles with multiple cameras, or the audio and images in videos.

• Continuity. This can be applied to domains where there are sequences with some continuity. For example, videos are sequences of images where most contiguous frames are almost equal. We can take images very close in time and assume they are very likely to be similar.

Discussion on negative examples

The loss functions we have seen thus far use both positive and negatives examples. We already gave a theoretical reason for this: if negative examples were not used, a trivial representation mapping everything to one vector would obtain perfect performance. There is empirical evidence showing that performance is increased from contrasting with many negative examples. For example, Chen et al. found that training with very large batch sizes greatly improved performance. In their setting, all other examples in the batch were considered as negative, and thus the number of negatives per example scaled with the batch size.

The concept of negative examples or, more generally, preventing representational collapse, is quite important in contrastive learning, and there are some topics around it that are worth discussing.

False negatives in self-supervised contrastive learning

In the data generation section we already talked at the possibility of false negatives when there is no supervision. This is because we are drawing from the full distribution of examples instead of the distribution of negative examples, creating a bias. Some work has been done on alleviating this while not requiring manual labels for negatives. While we won’t get into detail, the reader may find an example in the Debiased Contrastive loss proposed in .

Alleviating hardware bottlenecks for large amounts of negative samples

The use of examples in the same batch as negatives presents a major drawback in terms of computing resources. If we want to use many negative examples, we are forced to select a very large batch size, requiring a lot of GPU memory. Some work has been done on decoupling batch size and the number of negatives by sampling negatives from an offline memory bank. In this setting, an encoded representation is kept on-disk for some or all examples, and the loss function is not back-propagated through them. As the encoder is updated in the training process, the representations on-disk get outdated. The main difference between approaches lies in the method to keep these offline representations updated as the encoder is optimized.

Wu et al. sampled negative representations randomly from a memory bank with the full dataset. At the end of each epoch, all the representations in the memory bank are updated with the new checkpoint of the model.

He et al. proposed keeping a queue with a fixed number of mini-batches. After processing a mini-batch, the new examples are added to the queue, and the oldest mini-batch is removed. The queue is used to sample negative examples for the current mini-batch. Since this alone resulted in poor empirical performance, they separated the online encoder that is being trained from an offline encoder that produces the representations for the queue. The parameters of the offline encoder are updated through a momentum update rule with the parameters of the online one:

\[\label{eq:moco} \theta_{off} \leftarrow \alpha \theta_{off} + (1-\alpha) \theta_{on}, \qquad \alpha \in [0,1) .\]

This smoother update yielded better empirical performance.

Hard negative mining

While increasing the number of negative examples has been observed to improve performance, this might be due to the increased probability of finding meaningful negatives to learn from.

Conceptually, we might intuit that it is more difficult for a model to distinguish between a dog and a cat than between a dog and a building. In practice, we could select hard negative examples based on their representation. Examples with close representations are difficult to distinguish for the model. Kalantidis et al. make use of this, together with some data mixing techniques.

However, hard negative mining has some disadvantages, such as the increased time complexity from sampling close neighbours and the increased probability of drawing false negatives in the self-supervised setting as the encoder gets better.

Are negative examples really necessary?

If the only reason to use negative examples is to prevent the representations from collapsing onto one single vector, then other techniques to prevent it could allow us to remove negative examples.

The work by Grill et al. goes in this line. They used two neural networks, an online (predictive) network and a target network, similar to Deep Q Learning. They use only positive examples, and for each pair the online network tries to predict the metric representation from the target network. The parameters of the target network are updated after every iteration with an exponential moving average of the online parameters:

\[\theta_{target} \gets \alpha \theta_{target} + (1 - \alpha) \theta_{online} .\]

It is not obvious to me that this approach would not converge to a collapsed representation. The authors argue that the update to the target parameters is not exactly according to the gradient of the loss with respect to $\theta_{target}$. They did get good performance in downstream image classification tasks. There is some discussion on informal mediums (see this blog post and preprint ) on whether batch normalization is the cause of preventing representational collapse, but I haven’t found peer-reviewed work on the topic.

SimSiam is an even simpler technique that successfully avoided representational collapse. I deviate from their notation in the explanation to keep the one in this post. If $\hat{e}=e \circ h$ is the complete encoder onto the metric space, and $\hat{h}$ is an additional head they call the predictor, then their loss functions for similar instances $x_1$ and $x_2$ is given by the formula:

\[z_1 = \hat{e} (x_1),\quad z_2 = \hat{e} (x_2)\] \[p_1 = \hat{h} (z_1),\quad p_2 = \hat{h} (z_2)\] \[\mathcal{L} = \frac{D(p_1, SG(z_2))}{2} + \frac{D(p_2, SG(z_1))}{2} ,\]

where $SG$ is the stop-gradient function, preventing backpropagation through that branch of computation, and $D$ is the negative cosine similarity. Again, how this asymmetry through the additional head and stopping gradient propagation achieves its objective of avoiding representation collapse is not fully understood (to the best of my knowledge).

Bardes et al. proposed a much more intuitive approach, using VICReg (Variance-Invariance-Covariance regularization). Let $(z_1,z_1^\prime),\cdots,(z_n,z_n^\prime)$ be $d$-dimensional metric representations of positive pairs in a single batch. Let $C(Z)$ be the covariance matrix of the features for the matrix $Z=(z_1,\cdots,z_n) \in \mathbb{R}^{n \times d}$, that is,

\[C(Z) = \frac{1}{n-1} \sum_{i=1}^{n} (z_i - \overline{z})(z_i - \overline{z})^T ,\]

where $\overline{z} = \frac{1}{n} \sum_{i=1}^{n} z_i$ is the empirical mean. Then the loss function has the following three terms:

• Invariance. Forces the positive examples to be close together:

  \[\mathcal{I} = \frac{1}{n}\sum_{i=1}^{n} \| z_i - z_i^\prime \|^2 .\]

• Variance. Forces the variance of each feature to be positive, above some threshold value:

  \[\begin{gathered} \mathcal{V} = \frac{1}{d} \sum_{j=1}^{d} \max \left(0, \sqrt{C(Z)_{j,j} + \epsilon} \right) + \\ \frac{1}{d} \sum_{j=1}^{d} \max \left(0, \sqrt{C(Z^\prime)_{j,j} + \epsilon} \right) .\end{gathered}\]

• Covariance. Forces the features to be uncorrelated:

  \[\mathcal{C} = \frac{1}{d} \sum_{i,j=1; i\neq j}^{d} C(Z)_{i,j}^2 + \frac{1}{d} \sum_{i,j=1; i\neq j}^{d} C(Z^\prime)_{i,j}^2 .\]

The final loss is a weighted sum of the previous terms:

\[\mathcal{L} = \mathcal{I} + \lambda \mathcal{V} + \mu \mathcal{C} ,\]

where $\lambda$ and $\mu$ are positive hyperparameters. The workings of this approach are much more intuitive and clear: the regularization terms explicitly prevent the representation from collapsing, as collapsed representations have zero feature variance. Decorrelating the features might be useful for interpretability purposes.

Applications

We have already talked about the benefits of representation learning in general. But does contrastive learning have any particular advantages? This is what we discuss here.

The first advantage is that it provides other training ideas for representation learning. These techniques have shown great success, reaching state-of-the-art performance in semi-supervised and transfer learning. The SimCLR model for images is an example of this.

A second advantage is the metric representation that they learn. These representations can later be used in very efficientThere are techniques to approximately find the closest vectors to the input in a vector database, such as Locality-Sensitive Hashing or k-d Trees. vector searches, allowing us to search for examples in our database that are semantically similar to the input. Thinking of search engines for examples, we can now find texts that talk about something similar to our search prompt, without the need to use any common word. With multilingual models, we might even search semantically in different languages at the same time. We find many open source language models that have been trained for this semantic encoding, and that we can readily apply to any search application.

Although not exclusive to contrastive learning, another advantage is that these techniques allow us to align different encoders in a common representation space, in particular encoders of different modalities. This is the case of the CLIP model we have already talked about.

Finally, models trained through contrastive learning have zero-shot classification capabilities. If you have some anchor examples that represent a category, you can classify a new example by selecting the category with the closest metric representation. In the case of CLIP, since it has a text encoder, you can provide any textual label that describes the category, and apply this inference technique. If there are no textual encoders, you could still use a small set of labeled data to create a metric representation of each class. Of course, performance is not as good as with fully-trained models, specially for very niche categories, but it might still be useful with very few resources.

Conclusions

That it is for this post. I hope you found it useful and not too boring! As always, the best way to learn about anything is to get your hands dirty. In this case, you can try programming some training loop with a small dataset and a loss function, such as NT-Xent. You can also try programming some of the techniques to avoid representational collapse and see how they perform. Don’t doubt in hitting me up with any feedback or corrections!