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TransBoost: Improving the Best ImageNet Performance using Deep Transduction
Omer Belhasin Guy Bar-Shalom Ran El-Yaniv
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 .
Code Repositories
Benchmarks
| Benchmark | Methodology | Metrics |
|---|---|---|
| image-classification-on-cifar-10 | TransBoost-ResNet50 | Percentage correct: 97.61 |
| image-classification-on-dtd | TransBoost-ResNet50 | Accuracy: 76.49 |
| image-classification-on-fgvc-aircraft | TransBoost-ResNet50 | Accuracy: 83.80% |
| image-classification-on-flowers-102 | TransBoost-ResNet50 | Accuracy: 97.85% |
| image-classification-on-food-101-1 | TransBoost-ResNet50 | Accuracy (%): 84.30 |
| image-classification-on-imagenet | TransBoost-ResNet50-StrikesBack | Number of params: 25.56M Top 1 Accuracy: 81.15% |
| image-classification-on-imagenet | TransBoost-ResNet152 | Number of params: 60.19M Top 1 Accuracy: 80.64% |
| image-classification-on-imagenet | TransBoost-Swin-T | Number of params: 71.71M Top 1 Accuracy: 82.16% |
| image-classification-on-imagenet | TransBoost-ResNet50 | Top 1 Accuracy: 79.03% |
| image-classification-on-imagenet | TransBoost-ResNet18 | Number of params: 11.69M Top 1 Accuracy: 73.36% |
| image-classification-on-imagenet | TransBoost-ResNet34 | Number of params: 21.8M Top 1 Accuracy: 76.70% |
| image-classification-on-imagenet | TransBoost-ResNet101 | Number of params: 44.55M Top 1 Accuracy: 79.86% |
| image-classification-on-imagenet | TransBoost-EfficientNetB0 | Number of params: 5.29M Top 1 Accuracy: 78.60% |
| image-classification-on-imagenet | TransBoost-MobileNetV3-L | Number of params: 5.48M Top 1 Accuracy: 76.81% |
| image-classification-on-imagenet | TransBoost-ViT-S | Number of params: 22.05M Top 1 Accuracy: 83.67% |
| image-classification-on-imagenet | TransBoost-ConvNext-T | Number of params: 28.59M Top 1 Accuracy: 82.46% |
| image-classification-on-stanford-cars | TransBoost-ResNet50 | Accuracy: 90.80% |
| image-classification-on-sun397 | TransBoost-ResNet50 | Accuracy: 95.94% |
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