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 .
@article{arxiv.2205.13331,
title = {TransBoost: Improving the Best ImageNet Performance using Deep Transduction},
author = {Omer Belhasin and Guy Bar-Shalom and Ran El-Yaniv},
journal= {arXiv preprint arXiv:2205.13331},
year = {2023}
}