Research map / Label-efficient and robust learning / Semi-supervised learning
RETRIEVE: Coreset Selection for Efficient and Robust Semi-Supervised Learning
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Select an adaptive coreset of unlabeled data instead of training on the full unlabeled set | VAT · CIFAR-10 | speedup: 2.7x | Helped |
| Select an adaptive coreset of unlabeled data instead of training on the full unlabeled set | VAT · SVHN | speedup: 4.4x | Helped |
| Select an adaptive coreset of unlabeled data instead of training on the full unlabeled set | Mean-Teacher · CIFAR-10 | speedup: 2.9x | Helped |
| Select an adaptive coreset of unlabeled data instead of training on the full unlabeled set | Mean-Teacher · SVHN | speedup: 3.2x | Helped |
| Select an adaptive coreset of unlabeled data instead of training on the full unlabeled set | FixMatch · CIFAR-10 | speedup: 3.8x | Helped |
| Select an adaptive coreset of unlabeled data instead of training on the full unlabeled set | RETRIEVE + VAT · IMDB | speedup: 2.68x | Helped |
| Select an adaptive coreset of unlabeled data instead of training on the full unlabeled set | RETRIEVE + VAT · ELEC | speedup: 2.5x | Helped |
| Select an adaptive coreset rather than use DS3L | VAT on CIFAR-10 with class imbalance · CIFAR-10 with class imbalance | speedup: 5x | Helped |
| Select an adaptive coreset rather than use other baselines | VAT on CIFAR-10 with class imbalance · CIFAR-10 with class imbalance | accuracy: at least 1.5% | Helped |
Closest papers by meaning
- CGMatch: A Different Perspective of Semi-supervised Learning (CVPR 2025 · similarity 0.88)
- Erasing the Bias: Fine-Tuning Foundation Models for Semi-Supervised Learning (ICML 2024 · similarity 0.88)
- Enhancing Semi-Supervised Learning via Representative and Diverse Sample Selection (NeurIPS 2024 · similarity 0.88)
- Towards Realistic Model Selection for Semi-supervised Learning (ICML 2024 · similarity 0.87)
- Contrastive Credibility Propagation for Reliable Semi-supervised Learning (AAAI 2024 · similarity 0.87)
- Bi-Level Optimization for Semi-Supervised Learning with Pseudo-Labeling (AAAI 2025 · similarity 0.86)
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