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TransBoost: Improving the Best ImageNet Performance using Deep Transduction

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

Hall J (level 1) #912

Keywords: [ image classification ] [ Deep Transductive Learning ]


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: .

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