Papers › Long-Tail Learning with Foundation Model: Heavy Fine-Tuning Hurts
Long-Tail Learning with Foundation Model: Heavy Fine-Tuning Hurts
Jiang-Xin Shi, Tong Wei, Zhi Zhou, Jie-Jing Shao, Xin-Yan Han, Yu-Feng Li
The fine-tuning paradigm in addressing long-tail learning tasks has sparked significant interest since the emergence of foundation models. Nonetheless, how fine-tuning impacts performance in long-tail learning was not explicitly quantified. In this paper, we disclose that heavy fine-tuning may even lead to non-negligible performance deterioration on tail classes, and lightweight fine-tuning is more effective. The reason is attributed to inconsistent class conditions caused by heavy fine-tuning. With the observation above, we develop a low-complexity and accurate long-tail learning algorithms LIFT with the goal of facilitating fast prediction and compact models by adaptive lightweight fine-tuning. Experiments clearly verify that both the training time and the learned parameters are significantly reduced with more accurate predictive performance compared with state-of-the-art approaches. The implementation code is available at https://github.com/shijxcs/LIFT.
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Code
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Code Syntology ran Syntology
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Long-tail Learning | CIFAR-100-LT (ρ=10) | LIFT (ViT-B/16, ImageNet-21K pre-training) | Error Rate | 8.7 | #1 of 31 | Archive leaderboard | report |
| Long-tail Learning | CIFAR-100-LT (ρ=10) | LIFT (ViT-B/16, CLIP) | Error Rate | 15.1 | #4 of 31 | Archive leaderboard | report |
| Long-tail Learning | CIFAR-100-LT (ρ=100) | LIFT (ViT-B/16, ImageNet-21K pre-training) | Error Rate | 10.9 | #2 of 66 | Archive leaderboard | report |
| Long-tail Learning | CIFAR-100-LT (ρ=100) | LIFT (ViT-B/16, CLIP) | Error Rate | 18.3 | #3 of 66 | Archive leaderboard | report |
| Long-tail Learning | CIFAR-100-LT (ρ=50) | LIFT (ViT-B/16, ImageNet-21K pre-training) | Error Rate | 9.8 | #1 of 25 | Archive leaderboard | report |
| Long-tail Learning | CIFAR-100-LT (ρ=50) | LIFT (ViT-B/16, CLIP) | Error Rate | 16.9 | #4 of 25 | Archive leaderboard | report |
| Long-tail Learning | ImageNet-LT | LIFT (ViT-L/14) | Top-1 Accuracy | 82.9 | #1 of 69 | Archive leaderboard | report |
| Long-tail Learning | ImageNet-LT | LIFT (ViT-B/16) | Top-1 Accuracy | 78.3 | #4 of 69 | Archive leaderboard | report |
| Long-tail Learning | Places-LT | LIFT (ViT-L/14) | Top-1 Accuracy | 53.7 | #1 of 29 | Archive leaderboard | report |
| Long-tail Learning | Places-LT | LIFT (ViT-B/16) | Top-1 Accuracy | 52.2 | #2 of 29 | Archive leaderboard | report |
| Long-tail Learning | iNaturalist 2018 | LIFT (ViT-L/14@336px) | Top-1 Accuracy | 87.4% | #1 of 43 | Archive leaderboard | report |
| Long-tail Learning | iNaturalist 2018 | LIFT (ViT-L/14) | Top-1 Accuracy | 85.2% | #2 of 43 | Archive leaderboard | report |
| Long-tail Learning | iNaturalist 2018 | LIFT (ViT-B/16) | Top-1 Accuracy | 80.4% | #5 of 43 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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