HydraMix: Multi-Image Feature Mixing for Small Data Image Classification

TL;DR

HydraMix is a novel data augmentation method that creates new images by mixing multiple images from the same class, improving small dataset classification performance.

Contribution

We propose HydraMix, a new architecture for multi-image feature mixing and augmentation tailored for small data image classification tasks.

Findings

HydraMix outperforms state-of-the-art augmentation methods on small datasets.

The method effectively generates diverse training images from limited data.

A new text-image metric assesses dataset generality.

Abstract

Training deep neural networks requires datasets with a large number of annotated examples. The collection and annotation of these datasets is not only extremely expensive but also faces legal and privacy problems. These factors are a significant limitation for many real-world applications. To address this, we introduce HydraMix, a novel architecture that generates new image compositions by mixing multiple different images from the same class. HydraMix learns the fusion of the content of various images guided by a segmentation-based mixing mask in feature space and is optimized via a combination of unsupervised and adversarial training. Our data augmentation scheme allows the creation of models trained from scratch on very small datasets. We conduct extensive experiments on ciFAIR-10, STL-10, and ciFAIR-100. Additionally, we introduce a novel text-image metric to assess the generality of the augmented datasets. Our results show that HydraMix outperforms existing state-of-the-art methods for image classification on small datasets.