# Color-$S^{4}L$: Self-supervised Semi-supervised Learning with Image Colorization

Source: [Color-$S^{4}L$: Self-supervised Semi-supervised Learning with Image Colorization](<https://www.sota2.com/research/paper/68fa71cf1e209131cea53140>)

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

Data updated: 2025-10-26T02:12:32.787Z

Authors: Hanxiao Chen

Published: 2024-01-08T09:24:39.000Z

## Abstract

This work addresses the problem of semi-supervised image classification tasks with the integration of several effective self-supervised pretext tasks. Different from widely-used consistency regularization within semi-supervised learning, we explored a novel self-supervised semi-supervised learning framework (Color-$S^{4}L$) especially with image colorization proxy task and deeply evaluate performances of various network architectures in such special pipeline. Also, we demonstrated its effectiveness and optimal performance on CIFAR-10, SVHN and CIFAR-100 datasets in comparison to previous supervised and semi-supervised optimal methods.

## 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.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2401.03753>)

[PDF](<https://arxiv.org/pdf/2401.03753.pdf>)