LadderMIL: Multiple Instance Learning with Coarse-to-Fine Self-Distillation

arXiv:2502.02707 · cs.CV · Submitted 2025-02-04 · Read on arXiv

cs.CV

Submitted: 2025-02-04

Updated: 2026-09-25

Code: https://github.com/franksyng/LadderMIL

Terminology

• AUC — 49× in this paper · explained in 2 episodes
• Distillation — 24× in this paper · explained in 2 episodes
• Transformer — 19× in this paper · explained in 2 episodes
• Self-distillation — 16× in this paper · explained in 6 episodes
• Multiple Instance Learning — 13× in this paper
• Benchmark — 9× in this paper · explained in 2 episodes
• Contextual Encoding Generator — 8× in this paper
• Embeddings — 8× in this paper · explained in 2 episodes
• Foundation Model — 8× in this paper · explained in 3 episodes
• Benchmarking — 7× in this paper · explained in 3 episodes
• Ablation — 6× in this paper · explained in 1 episode
• Coarse-to-Fine Self-Distillation — 6× in this paper
• Ablation study — 5× in this paper · explained in 1 episode
• Contrastive Learning — 5× in this paper · explained in 5 episodes
• Knowledge Distillation — 5× in this paper · explained in 11 episodes
• Foundation Models — 4× in this paper · explained in 7 episodes
• Interpretability — 4× in this paper · explained in 15 episodes
• Multiple Instance Learning (MIL) — 4× in this paper · explained in 2 episodes
• Feature Extraction — 3× in this paper · explained in 2 episodes
• Loss Function — 2× in this paper · explained in 2 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Adaptability — 1× in this paper · explained in 1 episode
• Area Under the Curve (AUC) — 1× in this paper · explained in 1 episode
• Attention Mechanism — 1× in this paper · explained in 2 episodes
• Diffusion Model — 1× in this paper · explained in 8 episodes
• Diffusion Models — 1× in this paper · explained in 6 episodes
• Hypergraph — 1× in this paper · explained in 2 episodes
• Latent Diffusion Model — 1× in this paper · explained in 2 episodes
• Latent Diffusion Models — 1× in this paper · explained in 2 episodes
• Pyramid Position Encoding Generator — 1× in this paper
• Regularization — 1× in this paper · explained in 3 episodes
• Self-Supervised Learning — 1× in this paper · explained in 7 episodes
• Vision Transformer (ViT) — 1× in this paper · explained in 2 episodes

Sources

• Distilling the Knowledge in a Neural Network

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