Adversarial Training Methods for Semi-Supervised Text Classification
Paper
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1605.07725
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Published
Robustness and Generalization
Paper collections about improving the robustness and generalization of ML models
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Multi-Sample Dropout for Accelerated Training and Better Generalization
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SMART: Robust and Efficient Fine-Tuning for Pre-trained Natural Language Models through Principled Regularized Optimization
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Self-training with Noisy Student improves ImageNet classification
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Meta Pseudo Labels
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Adversarial Weight Perturbation Helps Robust Generalization
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Fine-Tuning Pre-trained Language Model with Weak Supervision: A Contrastive-Regularized Self-Training Approach
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