  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 .
