Equivariant Contrastive Learning (2111.00899v2)

Abstract: In state-of-the-art self-supervised learning (SSL) pre-training produces semantically good representations by encouraging them to be invariant under meaningful transformations prescribed from human knowledge. In fact, the property of invariance is a trivial instance of a broader class called equivariance, which can be intuitively understood as the property that representations transform according to the way the inputs transform. Here, we show that rather than using only invariance, pre-training that encourages non-trivial equivariance to some transformations, while maintaining invariance to other transformations, can be used to improve the semantic quality of representations. Specifically, we extend popular SSL methods to a more general framework which we name Equivariant Self-Supervised Learning (E-SSL). In E-SSL, a simple additional pre-training objective encourages equivariance by predicting the transformations applied to the input. We demonstrate E-SSL's effectiveness empirically on several popular computer vision benchmarks, e.g. improving SimCLR to 72.5% linear probe accuracy on ImageNet. Furthermore, we demonstrate usefulness of E-SSL for applications beyond computer vision; in particular, we show its utility on regression problems in photonics science. Our code, datasets and pre-trained models are available at https://github.com/rdangovs/essl to aid further research in E-SSL.

• The paper proposes the E-SSL framework that enforces equivariance for select transformations to enhance semantic representations.
• It empirically demonstrates that integrating equivariance with invariant SSL improves ImageNet linear probe accuracy to 72.5% and benefits regression tasks.
• The approach is broadly applicable to both discrete and continuous transformation groups, paving the way for diverse and specialized applications.

Overview of "Equivariant Contrastive Learning"

The paper "Equivariant Contrastive Learning" proposes an advancement in self-supervised learning (SSL) by introducing Equivariant Self-Supervised Learning (E-SSL). Traditional SSL techniques focus on representation learning that encourages invariance under specific transformations. However, the authors argue that invariance is a subset of a broader concept known as equivariance, where representations transform in a well-defined manner consistent with the input transformations. The paper explores a framework where both invariance and non-trivial equivariance are utilized to improve semantic representations, particularly in computer vision tasks.

Key Contributions

1. E-SSL Framework: The authors extend current SSL methods by advocating for Equivariant Self-Supervised Learning (E-SSL). The paper posits that encouraging equivariance for certain transformations, while maintaining invariance for others, enhances the semantic quality of the learned representations. In this framework, additional pre-training objectives predict transformations applied to inputs, which differs from the purely invariant SSL approaches.
2. Empirical Evaluation: The efficacy of E-SSL is empirically demonstrated across several computer vision benchmarks. Notably, the integration of E-SSL into SimCLR resulted in a linear probe accuracy of 72.5% on ImageNet, marking an improvement. Furthermore, the utility of E-SSL extends beyond vision tasks, proven by its effectiveness in regression tasks within photonics science.
3. Broad Applicability: E-SSL is shown to be applicable to groups of transformations which could be finite or continuous, enlarging its potential domain of applicability. The paper provides examples utilizing four-fold rotations, vertical flips, jigsaws, and Gaussian blurring to illustrate how particular transformations can impact representation learning uniquely.

Implications and Future Directions

From a practical standpoint, E-SSL poses potential advantages in scenarios where task-specific transformations are known, making the method adaptable to specialized applications beyond the typical computer vision tasks. Theoretically, E-SSL reinforces the importance of considering the nature of transformations in learning representations. Instead of a binary distinction between invariance and non-invariance, E-SSL proposes a spectrum involving equivariance.

Future research might explore an automated approach to discover transformations beneficial for equivariance, rather than manually setting them. This could pave the way for adapting the E-SSL framework to natural language processing, audio analysis, and other domains where the transformations are less understood.

By aligning representation learning more closely with the intrinsic structures and transformations of the data, E-SSL advances the possibilities of self-supervised learning, pushing towards a more nuanced understanding and application of equivariant representations.