TransBoost: Improving the Best ImageNet Performance using Deep Transduction

Omer Belhasin, Guy Bar-Shalom, Ran El-Yaniv

Keywords: Deep Transductive Learning, Image Classification

Abstract:

This paper deals with deep transductive learning, and proposes TransBoost as a procedure for fine-tuning any deep neural model to improve its performance on any (unlabeled) test set provided at training time. TransBoost is inspired by a large margin principle and is efficient and simple to use. Our method significantly improves the ImageNet classification performance on a wide range of architectures, such as ResNets, MobileNetV3-L, EfficientNetB0, ViT-S, and ConvNext-T, leading to state-of-the-art transductive performance.
Additionally we show that TransBoost is effective on a wide variety of image classification datasets. The implementation of TransBoost is provided at: https://github.com/omerb01/TransBoost .

Community Implementations: [ 1 code implementation](https://www.catalyzex.com/paper/transboost-improving-the-best-imagenet/code)