# Continual Contrastive Learning for Image Classification

Source: [Continual Contrastive Learning for Image Classification](<https://www.sota2.com/research/paper/68fa6dd81e209131cea3c5c9>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T00:44:11.594Z

Authors: Zhiwei Lin, Yongtao Wang, Hongxiang Lin

Published: 2021-07-05T03:53:42.000Z

## Abstract

Recently, self-supervised representation learning gives further development in multimedia technology. Most existing self-supervised learning methods are applicable to packaged data. However, when it comes to streamed data, they are suffering from a catastrophic forgetting problem, which is not studied extensively. In this paper, we make the first attempt to tackle the catastrophic forgetting problem in the mainstream self-supervised methods, i.e., contrastive learning methods. Specifically, we first develop a rehearsal-based framework combined with a novel sampling strategy and a self-supervised knowledge distillation to transfer information over time efficiently. Then, we propose an extra sample queue to help the network separate the feature representations of old and new data in the embedding space. Experimental results show that compared with the naive self-supervised baseline, which learns tasks one by one without taking any technique, we improve the image classification accuracy by 1.60% on CIFAR-100, 2.86% on ImageNet-Sub, and 1.29% on ImageNet-Full under 10 incremental steps setting. Our code will be available at https://github.com/VDIGPKU/ContinualContrastiveLearning.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Unsupervised Continual Clustering on CIFAR-100](<https://www.sota2.com/research/sota/unsupervised-continual-clustering-on-cifar-100.md>): Unsupervised Continual Clustering; CIFAR-100; Accuracy: 19.59.

- [Unsupervised Continual Clustering on ImageNet 100](<https://www.sota2.com/research/sota/unsupervised-continual-clustering-on-imagenet-100.md>): Unsupervised Continual Clustering; ImageNet-100; Accuracy (ACC): 24.03.

- [Unsupervised Continual Clustering on CIFAR-10](<https://www.sota2.com/research/sota/unsupervised-continual-clustering-on-cifar-10.md>): Unsupervised Continual Clustering; CIFAR-10; Accuracy (ACC): 36.56.

- [Unsupervised Continual Clustering on Tiny-ImageNet](<https://www.sota2.com/research/sota/unsupervised-continual-clustering-on-tiny-imagenet.md>): Unsupervised Continual Clustering; Tiny-ImageNet; Clustering Accuracy (ACC): 8.21.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2107.01776>)

[PDF](<https://arxiv.org/pdf/2107.01776.pdf>)