Improving Knowledge Distillation Under Unknown Covariate Shift Through Confidence-Guided Data Augmentation

arXiv:2506.02294 · cs.CV · Submitted 2025-06-02 · Read on arXiv

cs.CV

Submitted: 2025-06-02

Updated: 2026-09-15

Code: https://github.com/pytorch/vision

Terminology

• Data Augmentation — 66× in this paper · explained in 7 episodes
• Distillation — 50× in this paper · explained in 2 episodes
• Covariate Shift — 35× in this paper · explained in 2 episodes
• Knowledge Distillation — 35× in this paper · explained in 11 episodes
• Diffusion Model — 28× in this paper · explained in 8 episodes
• Accuracy — 23× in this paper · explained in 1 episode
• Diffusion Models — 17× in this paper · explained in 6 episodes
• alpha — 16× in this paper · explained in 1 episode
• Robustness — 14× in this paper · explained in 5 episodes
• Generalization — 11× in this paper · explained in 13 episodes
• Consistency — 8× in this paper · explained in 3 episodes
• Foundation Model — 6× in this paper · explained in 3 episodes
• Foundation Models — 6× in this paper · explained in 7 episodes
• Ablation — 5× in this paper · explained in 1 episode
• Regularization — 5× in this paper · explained in 3 episodes
• Adam — 3× in this paper · explained in 2 episodes
• Embeddings — 3× in this paper · explained in 2 episodes
• Cosine Similarity — 2× in this paper · explained in 3 episodes
• Generative Model — 2× in this paper · explained in 2 episodes
• Generative Models — 2× in this paper · explained in 2 episodes
• Loss Function — 2× in this paper · explained in 2 episodes
• Transformer — 2× in this paper · explained in 2 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• AdamW Optimizer — 1× in this paper · explained in 1 episode
• Adaptation — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Decode — 1× in this paper · explained in 2 episodes
• Domain Adaptation — 1× in this paper · explained in 5 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Flow Matching — 1× in this paper · explained in 8 episodes
• Hallucination — 1× in this paper · explained in 3 episodes
• Latent Diffusion Model — 1× in this paper · explained in 2 episodes
• Latent Diffusion Models — 1× in this paper · explained in 2 episodes
• Latent Space — 1× in this paper · explained in 7 episodes
• Multimodal Dataset — 1× in this paper · explained in 2 episodes
• Synthetic Data Generation — 1× in this paper · explained in 5 episodes
• Zero-Shot Transfer — 1× in this paper · explained in 4 episodes

Sources

• Consistency-diversity-realism Pareto fronts of conditional image generative models
• Prompt-to-Prompt Image Editing with Cross Attention Control
• Distilling the Knowledge in a Neural Network
• Challenges and Opportunities in Improving Worst-Group Generalization in Presence of Spurious Features
• FLUX.1 Kontext: Flow Matching for In-Context Image Generation and Editing in Latent Space
• Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference
• AWADA: Attention-Weighted Adversarial Domain Adaptation for Object Detection
• BEiT v2: Masked Image Modeling with Vector-Quantized Visual Tokenizers
• Qwen-Image Technical Report
• Qwen3 Technical Report
• IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Models

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