TransBoost: Improving the Best ImageNet Performance using Deep Transduction

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

2022-05-26Image Classification

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. 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 .

Results

Task	Dataset	Metric	Value	Model
Image Classification	DTD	Accuracy	76.49	TransBoost-ResNet50
Image Classification	CIFAR-10	Percentage correct	97.61	TransBoost-ResNet50
Image Classification	Food-101	Accuracy (%)	84.3	TransBoost-ResNet50

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