# Learning to Obstruct Few-Shot Image Classification over Restricted Classes

Source: [Learning to Obstruct Few-Shot Image Classification over Restricted Classes](<https://www.sota2.com/research/paper/68fa74f81e209131cea64f53>)

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

Data updated: 2025-10-28T13:57:09.782Z

Authors: Amber Yijia Zheng, Chiao-An Yang, Raymond A. Yeh

Published: 2024-09-28T02:16:38.000Z

## Abstract

Advancements in open-source pre-trained backbones make it relatively easy to fine-tune a model for new tasks. However, this lowered entry barrier poses potential risks, e.g., bad actors developing models for harmful applications. A question arises: Is possible to develop a pre-trained model that is difficult to fine-tune for certain downstream tasks? To begin studying this, we focus on few-shot classification (FSC). Specifically, we investigate methods to make FSC more challenging for a set of restricted classes while maintaining the performance of other classes. We propose to meta-learn over the pre-trained backbone in a manner that renders it a ''poor initialization''. Our proposed Learning to Obstruct (LTO) algorithm successfully obstructs four FSC methods across three datasets, including ImageNet and CIFAR100 for image classification, as well as CelebA for attribute classification.

## 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/2409.19210>)

[PDF](<https://arxiv.org/pdf/2409.19210.pdf>)