# A Strong Inductive Bias: Gzip for binary image classification

Source: [A Strong Inductive Bias: Gzip for binary image classification](<https://www.sota2.com/research/paper/68fa71d71e209131cea53464>)

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

Data updated: 2025-10-26T02:13:28.899Z

Authors: Marco Scilipoti, Marina Fuster, Rodrigo Ramele

Published: 2024-01-14T23:35:14.000Z

## Abstract

Deep learning networks have become the de-facto standard in Computer Vision for industry and research. However, recent developments in their cousin, Natural Language Processing (NLP), have shown that there are areas where parameter-less models with strong inductive biases can serve as computationally cheaper and simpler alternatives. We propose such a model for binary image classification: a nearest neighbor classifier combined with a general purpose compressor like Gzip. We test and compare it against popular deep learning networks like Resnet, EfficientNet and Mobilenet and show that it achieves better accuracy and utilizes significantly less space, more than two order of magnitude, within a few-shot setting. As a result, we believe that this underlines the untapped potential of models with stronger inductive biases in few-shot scenarios.

## 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/2401.07392>)

[PDF](<https://arxiv.org/pdf/2401.07392.pdf>)