# Self-supervised Learning with Local Attention-Aware Feature

Source: [Self-supervised Learning with Local Attention-Aware Feature](<https://www.sota2.com/research/paper/68fa6a7c1e209131cea2885f>)

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

Data updated: 2025-10-25T23:26:28.956Z

Authors: Trung X. Pham, Rusty John Lloyd Mina, Dias Issa, Chang D. Yoo

Published: 2021-08-01T15:26:30.000Z

## Abstract

In this work, we propose a novel methodology for self-supervised learning for generating global and local attention-aware visual features. Our approach is based on training a model to differentiate between specific image transformations of an input sample and the patched images. Utilizing this approach, the proposed method is able to outperform the previous best competitor by 1.03% on the Tiny-ImageNet dataset and by 2.32% on the STL-10 dataset. Furthermore, our approach outperforms the fully-supervised learning method on the STL-10 dataset. Experimental results and visualizations show the capability of successfully learning global and local attention-aware visual representations.

## 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/2108.00475>)

[PDF](<https://arxiv.org/pdf/2108.00475.pdf>)