# AdCo: Adversarial Contrast for Efficient Learning of Unsupervised Representations from Self-Trained Negative Adversaries

arXiv: https://arxiv.org/abs/2011.08435 · HTML: https://mlsanity.com/paper/2011.08435/

## Key facts

- Cited by 90 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Nov 2020.

## Built on by (most cited first, 30 of 90)

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- [Can contrastive learning avoid shortcut solutions?](https://mlsanity.com/paper/2106.11230/) (2021)
- [Large-scale Unsupervised Semantic Segmentation](https://mlsanity.com/paper/2106.03149/) (2021)
- [Contrastive Learning with Stronger Augmentations](https://mlsanity.com/paper/2104.07713/) (2021)
- [Towards the Generalization of Contrastive Self-Supervised Learning](https://mlsanity.com/paper/2111.00743/) (2021)
- [ReSSL: Relational Self-Supervised Learning with Weak Augmentation](https://mlsanity.com/paper/2107.09282/) (2021)
- [Twin Contrastive Learning for Online Clustering](https://mlsanity.com/paper/2210.11680/) (2022)
- [Learning Representation for Clustering via Prototype Scattering and Positive Sampling](https://mlsanity.com/paper/2111.11821/) (2021)
- [Self-Contrastive Learning with Hard Negative Sampling for Self-supervised Point Cloud Learning](https://mlsanity.com/paper/2107.01886/) (2021)
- [Semi-Supervised and Unsupervised Deep Visual Learning: A Survey](https://mlsanity.com/paper/2208.11296/) (2022)
- [Exploring the Equivalence of Siamese Self-Supervised Learning via A Unified Gradient Framework](https://mlsanity.com/paper/2112.05141/) (2021)
- [Self-Supervised Learning Disentangled Group Representation as Feature](https://mlsanity.com/paper/2110.15255/) (2021)
- [Instance-wise Hard Negative Example Generation for Contrastive Learning in Unpaired Image-to-Image Translation](https://mlsanity.com/paper/2108.04547/) (2021)
- [Improving Contrastive Learning by Visualizing Feature Transformation](https://mlsanity.com/paper/2108.02982/) (2021)
- [HCSC: Hierarchical Contrastive Selective Coding](https://mlsanity.com/paper/2202.00455/) (2022)
- [Exploring Patch-wise Semantic Relation for Contrastive Learning in Image-to-Image Translation Tasks](https://mlsanity.com/paper/2203.01532/) (2022)
- [Towards Unsupervised Domain Generalization](https://mlsanity.com/paper/2107.06219/) (2021)
- [Know Your Self-supervised Learning: A Survey on Image-based Generative and Discriminative Training](https://mlsanity.com/paper/2305.13689/) (2023)
- [Rethinking the Augmentation Module in Contrastive Learning: Learning Hierarchical Augmentation Invariance with Expanded Views](https://mlsanity.com/paper/2206.00227/) (2022)
- [Open-world Machine Learning: Applications, Challenges, and Opportunities](https://mlsanity.com/paper/2105.13448/) (2021)
- [Unsupervised Domain Generalization by Learning a Bridge Across Domains](https://mlsanity.com/paper/2112.02300/) (2021)
- [Towards Discriminative Representation: Multi-view Trajectory Contrastive Learning for Online Multi-object Tracking](https://mlsanity.com/paper/2203.14208/) (2022)
- [A Practical Contrastive Learning Framework for Single-Image Super-Resolution](https://mlsanity.com/paper/2111.13924/) (2021)
- [Contrastive Deep Supervision](https://mlsanity.com/paper/2207.05306/) (2022)
- [Multi-Mode Online Knowledge Distillation for Self-Supervised Visual Representation Learning](https://mlsanity.com/paper/2304.06461/) (2023)
- [Connecting Joint-Embedding Predictive Architecture with Contrastive Self-supervised Learning](https://mlsanity.com/paper/2410.19560/) (2024)
- [Similarity Contrastive Estimation for Self-Supervised Soft Contrastive Learning](https://mlsanity.com/paper/2111.14585/) (2021)

## Abstract (arXiv)

Contrastive learning relies on constructing a collection of negative examples that are sufficiently hard to discriminate against positive queries when their representations are self-trained. Existing contrastive learning methods either maintain a queue of negative samples over minibatches while only a small portion of them are updated in an iteration, or only use the other examples from the current minibatch as negatives. They could not closely track the change of the learned representation over iterations by updating the entire queue as a whole, or discard the useful information from the past minibatches. Alternatively, we present to directly learn a set of negative adversaries playing against the self-trained representation. Two players, the representation network and negative adversaries, are alternately updated to obtain the most challenging negative examples against which the representation of positive queries will be trained to discriminate. We further show that the negative adversaries are updated towards a weighted combination of positive queries by maximizing the adversarial contrastive loss, thereby allowing them to closely track the change of representations over time. Experiment results demonstrate the proposed Adversarial Contrastive (AdCo) model not only achieves superior performances (a top-1 accuracy of 73.2% over 200 epochs and 75.7% over 800 epochs with linear evaluation on ImageNet), but also can be pre-trained more efficiently with fewer epochs.