# Improving Transformation Invariance in Contrastive Representation Learning

arXiv: https://arxiv.org/abs/2010.09515 · HTML: https://mlsanity.com/paper/2010.09515/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Oct 2020.

## Built on by (most cited first, 16 of 16)

- [Equivariant Imaging: Learning Beyond the Range Space](https://mlsanity.com/paper/2103.14756/) (2021)
- [Equivariant Contrastive Learning](https://mlsanity.com/paper/2111.00899/) (2021)
- [Data augmentation in Bayesian neural networks and the cold posterior effect](https://mlsanity.com/paper/2106.05586/) (2021)
- [Improving Self-Supervised Learning by Characterizing Idealized Representations](https://mlsanity.com/paper/2209.06235/) (2022)
- [Provably Strict Generalisation Benefit for Invariance in Kernel Methods](https://mlsanity.com/paper/2106.02346/) (2021)
- [Learning Instance-Specific Augmentations by Capturing Local Invariances](https://mlsanity.com/paper/2206.00051/) (2022)
- [On Contrastive Representations of Stochastic Processes](https://mlsanity.com/paper/2106.10052/) (2021)
- [Integrating Prior Knowledge in Contrastive Learning with Kernel](https://mlsanity.com/paper/2206.01646/) (2022)
- [Incorporating Unlabelled Data into Bayesian Neural Networks](https://mlsanity.com/paper/2304.01762/) (2023)
- [A Low Rank Promoting Prior for Unsupervised Contrastive Learning](https://mlsanity.com/paper/2108.02696/) (2021)
- [Mean Embeddings with Test-Time Data Augmentation for Ensembling of Representations](https://mlsanity.com/paper/2106.08038/) (2021)
- [Improving Self-supervised Learning with Automated Unsupervised Outlier Arbitration](https://mlsanity.com/paper/2112.08132/) (2021)
- [Evaluating Self-Supervised Learning via Risk Decomposition](https://mlsanity.com/paper/2302.03068/) (2023)
- [Unleash Model Potential: Bootstrapped Meta Self-supervised Learning](https://mlsanity.com/paper/2308.14267/) (2023)
- [Genetic InfoMax: Exploring Mutual Information Maximization in High-Dimensional Imaging Genetics Studies](https://mlsanity.com/paper/2309.15132/) (2023)
- [DReS: Dual Reconstruction Smoothing for Functional Regularization](https://mlsanity.com/paper/2510.00253/) (2025)

## Abstract (arXiv)

We propose methods to strengthen the invariance properties of representations obtained by contrastive learning. While existing approaches implicitly induce a degree of invariance as representations are learned, we look to more directly enforce invariance in the encoding process. To this end, we first introduce a training objective for contrastive learning that uses a novel regularizer to control how the representation changes under transformation. We show that representations trained with this objective perform better on downstream tasks and are more robust to the introduction of nuisance transformations at test time. Second, we propose a change to how test time representations are generated by introducing a feature averaging approach that combines encodings from multiple transformations of the original input, finding that this leads to across the board performance gains. Finally, we introduce the novel Spirograph dataset to explore our ideas in the context of a differentiable generative process with multiple downstream tasks, showing that our techniques for learning invariance are highly beneficial.