Vision Models Can Be Efficiently Specialized via Few-Shot Task-Aware Compression

TL;DR

This paper introduces TACO, a simple and efficient method for compressing large vision models into smaller, specialized models using few-shot learning, enabling faster inference with minimal accuracy loss on specific tasks.

Contribution

The paper presents TACO, a novel few-shot task-aware compression technique that effectively reduces model size and computational cost while maintaining high accuracy on specialized vision tasks.

Findings

TACO achieves up to 20x parameter reduction.

Inference speed increases up to 3x.

Specialized models remain accuracy-competitive.

Abstract

Recent vision architectures and self-supervised training methods enable vision models that are extremely accurate and general, but come with massive parameter and computational costs. In practical settings, such as camera traps, users have limited resources, and may fine-tune a pretrained model on (often limited) data from a small set of specific categories of interest. These users may wish to make use of modern, highly-accurate models, but are often computationally constrained. To address this, we ask: can we quickly compress large generalist models into accurate and efficient specialists? For this, we propose a simple and versatile technique called Few-Shot Task-Aware Compression (TACO). Given a large vision model that is pretrained to be accurate on a broad task, such as classification over ImageNet-22K, TACO produces a smaller model that is accurate on specialized tasks, such as classification across vehicle types or animal species. Crucially, TACO works in few-shot fashion, i.e. only a few task-specific samples are used, and the procedure has low computational overheads. We validate TACO on highly-accurate ResNet, ViT/DeiT, and ConvNeXt models, originally trained on ImageNet, LAION, or iNaturalist, which we specialize and compress to a diverse set of "downstream" subtasks. TACO can reduce the number of non-zero parameters in existing models by up to 20x relative to the original models, leading to inference speedups of up to 3$\times$, while remaining accuracy-competitive with the uncompressed models on the specialized tasks.