The Taub Faculty of Computer Science Events and Talks
Omer Belhasin (M.Sc. Thesis Seminar)
Wednesday, 15.11.2023, 13:00
Advisor: Prof. Ran El-Yaniv
This lecture is about our paper that was published in NeurIPS 2022. 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.