# Image Enhanced Rotation Prediction for Self-Supervised Learning

Source: [Image Enhanced Rotation Prediction for Self-Supervised Learning](<https://www.sota2.com/research/paper/68fa6a231e209131cea26465>)

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

Data updated: 2025-10-25T23:17:37.192Z

Authors: Shin'ya Yamaguchi, Sekitoshi Kanai, Tetsuya Shioda, Shoichiro Takeda

Published: 2019-12-25T06:11:35.000Z

## Abstract

The rotation prediction (Rotation) is a simple pretext-task for self-supervised learning (SSL), where models learn useful representations for target vision tasks by solving pretext-tasks. Although Rotation captures information of object shapes, it hardly captures information of textures. To tackle this problem, we introduce a novel pretext-task called image enhanced rotation prediction (IE-Rot) for SSL. IE-Rot simultaneously solves Rotation and another pretext-task based on image enhancement (e.g., sharpening and solarizing) while maintaining simplicity. Through the simultaneous prediction of rotation and image enhancement, models learn representations to capture the information of not only object shapes but also textures. Our experimental results show that IE-Rot models outperform Rotation on various standard benchmarks including ImageNet classification, PASCAL-VOC detection, and COCO detection/segmentation.

## 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/1912.11603>)

[PDF](<https://arxiv.org/pdf/1912.11603.pdf>)