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аналитика - Knowledge Distillation Techniques
Scale Decoupled Distillation: Enhancing Logit Knowledge Transfer for Improved Performance
Decoupling logit outputs at different scales enhances knowledge transfer, improving student performance.
Deep Classifier Mimicry without Data Access: A Model-Agnostic Approach
The authors propose Contrastive Abductive Knowledge Extraction (CAKE) as a model-agnostic method to mimic deep classifiers without access to original data, paving the way for broad application.
Knowledge Distillation Based on Transformed Teacher Matching: A Detailed Analysis
The author explores a variant of Knowledge Distillation without temperature scaling on the student side, known as Transformed Teacher Matching (TTM), to improve model generalization. Additionally, Weighted TTM (WTTM) is introduced as an effective distillation approach.
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