Addressing challenges in real-world image classification : long-tailed distribution and knowledge distillation
Bibliographic record
Abstract
In computer vision, image classification has progressed rapidly with deep learning over the ten years. However, in the real world, we still face challenges to apply them when the datasets are highly imbalanced, or in some situations to deploy large networks. From the data perspective, in this thesis, we aim to improve data augmentations for long-tailed image classification, where only a few semantic classes possess many samples while most other classes have only a few samples. We propose a novel Hybrid Mixup strategy to increase the sample amount and diversity, where we uncover the efficacy of mixup in the latent space of StyleGAN2. Compared with the traditional mixup method on real images, the mixup images generated from the interpolated latent codes have better quality. Experiments on CIFAR-10-LT, CIFAR-100-LT demonstrate that our proposed Hybrid Mixup consistently boosts the head-, medium- and tail-class classification accuracy compared with the traditional mixup method on real images only. Moreover, our results are on par with the state of the arts or even surpass them in some settings. From the model viewpoint, we particularly research the knowledge distillation, which leverages large models to distill enriched knowledge into smaller ones. Here we focus on the scenario where teachers output one-hot predictions only. We find it still possible for students to boost classification accuracy by directly learning from these one-hot predictions. We further propose Patched One-hot Distillation that models empirical probability for teachers to capture the inter-class relationship. Experiments on CIFAR-100 and ImageNet datasets demonstrate that our proposed method helps students learn better than the baseline that directly learns from both the ground-truth labels and the predictions from teachers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".