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TransBoost: Improving the Best ImageNet Performance using Deep Transduction

2022-05-26 13:09:29
Omer Belhasin, Guy Bar-Shalom, Ran El-Yaniv
arXiv_AI
       
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. The ImageNet classification performance is consistently and significantly improved with TransBoost on many architectures such as ResNets, MobileNetV3-L, EfficientNetB0, ViT-S, and ConvNext-T. Additionally we show that TransBoost is effective on a wide variety of image classification datasets.

Abstract (translated)

URL

https://arxiv.org/abs/2205.13331

PDF

https://arxiv.org/pdf/2205.13331.pdf

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