mlsanityData to 7 Oct 2026

TransBoost: Improving the Best ImageNet Performance using Deep Transduction

arXiv 2205.13331 ↗First posted 26 May 2022cs.CV cs.AI cs.LGcited by 3

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Abstract (arXiv)

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 .

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 23 of its 40 references that are in the map. First posted on arXiv 26 May 2022.

Builds on

23 of 40 references.

References · 23

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11 more
  1. 2019Searching for MobileNetV3cited by 1,984
  2. 2013Fine-Grained Visual Classification of Aircraftcited by 1,701
  3. 2020Big Self-Supervised Models are Strong Semi-Supervised Learnerscited by 1,375
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Built on by

Cited by year

Citing papers per year: 2024: 2; 2025: 1.

3 papers.

  1. 2024Boosting Vision-Language Models with Transductioncited by 36
  2. 2025DefTransNet: A Transformer-based Method for Non-Rigid Point Cloud Registration in the Simulation of Soft Tissue Deformationcited by 2
  3. 2024PARMESAN: Parameter-Free Memory Search and Transduction for Dense Prediction Taskscited by 1