MixCo: Mix-up Contrastive Learning for Visual Representation

Abstract

Contrastive learning has shown remarkable results in recent self-supervised approaches for visual representation. By learning to contrast positive pairs' representation from the corresponding negatives pairs, one can train good visual representations without human annotations. This paper proposes Mix-up Contrast (MixCo), which extends the contrastive learning concept to semi-positives encoded from the mix-up of positive and negative images. MixCo aims to learn the relative similarity of representations, reflecting how much the mixed images have the original positives. We validate the efficacy of MixCo when applied to the recent self-supervised learning algorithms under the standard linear evaluation protocol on TinyImageNet, CIFAR10, and CIFAR100. In the experiments, MixCo consistently improves test accuracy. Remarkably, the improvement is more significant when the learning capacity (e.g., model size) is limited, suggesting that MixCo might be more useful in real-world scenarios. The code is available at: https://github.com/Lee-Gihun/MixCo-Mixup-Contrast.

accepted in NeurIPS 2020 Workshop on Self-Supervised Learning

References in corpus (13)

• A Simple Framework for Contrastive Learning of Visual Representations
• mixup: Beyond Empirical Risk Minimization
• Bootstrap your own latent: A new approach to self-supervised Learning
• Language Models are Few-Shot Learners
• Improved Baselines with Momentum Contrastive Learning
• Unsupervised Learning of Visual Features by Contrasting Cluster Assignments
• Momentum Contrast for Unsupervised Visual Representation Learning
• Data-Efficient Image Recognition with Contrastive Predictive Coding
• Contrastive Multiview Coding
• Prototypical Contrastive Learning of Unsupervised Representations
• Manifold Mixup: Better Representations by Interpolating Hidden States
• Big Self-Supervised Models are Strong Semi-Supervised Learners
• What makes instance discrimination good for transfer learning?

Cited by in corpus (14)

• Patch-Mix Contrastive Learning with Audio Spectrogram Transformer on Respiratory Sound Classification
• i-Mix: A Domain-Agnostic Strategy for Contrastive Representation Learning
• Unleashing the Power of Contrastive Self-Supervised Visual Models via Contrast-Regularized Fine-Tuning
• Universum-inspired Supervised Contrastive Learning
• It Takes Two to Tango: Mixup for Deep Metric Learning
• Towards Domain-Agnostic Contrastive Learning
• MixSiam: A Mixture-based Approach to Self-supervised Representation Learning
• Understanding Hyperbolic Metric Learning through Hard Negative Sampling
• A Theory-Driven Self-Labeling Refinement Method for Contrastive Representation Learning
• Improving Contrastive Learning by Visualizing Feature Transformation
• DREAM: Visual Decoding from Reversing Human Visual System
• Motion-aware Contrastive Video Representation Learning via Foreground-background Merging
• Representation Synthesis by Probabilistic Many-Valued Logic Operation in Self-Supervised Learning
• Constructing Contrastive samples via Summarization for Text Classification with limited annotations