SimCLR is a simple framework for contrastive learning of visual representations.

## Key Contributions

- Composition of data augmentation to form positive pairs
- introduce a learnable nonlinear transformation between the representation and the contrastive loss substantially improves the quality of the learned representations
- Contrastive learning benefits from larger batch sizes and more training steps compared to supervised learning

## Data Augmentation

A stochastic data augmentation module is introduced to produce two correlated views of the same example, denoted \(\tilde{x}_i\) and \(\tilde{x}_j\), which is considered a positive pair. Some of these augmentations include:

- random cropping
- random color distortions
- random Gaussian blur

A neural network encoder \(f(\cdot)\) extracts representation vectors from augmented data examples.

A small network projection head \(g(\cdot)\) maps representations to the space where contrastive loss is applied.

The loss function (normalized temperature-scaled cross entropy loss) is applied on the output of \(g(\cdot)\).

A minibatch of N examples is sampled, resulting in \(2N\) data-points. The other 2(N-1) augmented examples within the minibatch is used as negative examples.

\begin{equation} \ell_{i, j}=-\log \frac{\exp \left(\operatorname{sim}\left(\boldsymbol{z}_{i}, \boldsymbol{z}_{j}\right) / \tau\right)}{\sum_{k=1}^{2 N} \mathbb{1}_{[k \neq i]} \exp \left(\operatorname{sim}\left(\boldsymbol{z}_{i}, \boldsymbol{z}_{k}\right) / \tau\right)} \end{equation}

## The Importance of the Projection Head

It is conjectured that the projection head \(g(\cdot)\) is important due to loss of information induced by the contrastive loss. \(z = g(h)\) is trained to be invariant to the data transformation. Thus \(g\) can remove information that may be useful for the downstream task, such as color or orientation of objects.

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