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

TransBoost: Improving the Best ImageNet Performance using Deep Transduction