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Intra-class patch swap for self-distillation
Hongjun Choi
, Eun Som Jeon
, Ankita Shukla
,
Pavan Turaga
Arts, Media and Engineering, School of (AME)
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Keyphrases
Self-distillation
100%
Distillation
75%
Knowledge Distillation
75%
Highly Effective
25%
Deep Learning Model
25%
Confidence Level
25%
Additional Training
25%
Training Procedure
25%
Object Detection
25%
Image Classification
25%
Teacher Networks
25%
Conventional Knowledge
25%
High Capacity
25%
Model Explanation
25%
Semantic Segmentation
25%
Single Model
25%
Memory Storage
25%
Student Model
25%
Predictive Distribution
25%
Segment Detection
25%
Architectural Complex
25%
Auxiliary Subunit
25%
Complex Training
25%
Small Edges
25%
Training Cost
25%
Storage Requirements
25%
Semantic Objects
25%
Learnable Parameters
25%
Student Network
25%
Generating Pair
25%
Computer Science
Self-Distillation
100%
Knowledge Distillation
75%
Object Detection
25%
Deep Learning Model
25%
Image Classification
25%
Image Segmentation
25%
Storage Requirement
25%
Confidence Level
25%
Predictive Distribution
25%
Student Model
25%
Chemical Engineering
Auxiliaries
100%
Deep Learning Method
100%
Object Detection
100%