Knowledge Distillation
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Knowledge distillation is the process of transferring knowledge from a large model to a smaller one. While large models (such as very deep neural networks or ensembles of many models) have higher knowledge capacity than small models, this capacity might not be fully utilized.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
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DistiLLM: Towards Streamlined Distillation for Large Language Models
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Embedding Transfer with Label Relaxation for Improved Metric Learning
https://github.com/tjddus9597/Proxy-Anchor-CVPR2020
Embedding Transfer with Label Relaxation for Improved Metric Learning
https://github.com/tjujianyu/rrl
Rich Feature Construction for the Optimization-Generalization Dilemma
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Torchreid: A Library for Deep Learning Person Re-Identification in Pytorch
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NATS-Bench: Benchmarking NAS Algorithms for Architecture Topology and Size
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SaiT: Sparse Vision Transformers through Adaptive Token Pruning
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Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks
https://github.com/toufiksk/darknet
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https://github.com/tracy6955/IBM_seizure_data
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https://github.com/trae1oung/dyprag
From Instance Training to Instruction Learning: Task Adapters Generation from Instructions
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https://github.com/tristangomez44/metrics-saliency-maps
Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks
https://github.com/trongnghia00/darknet
https://github.com/trougnouf/mthesis-denoise
https://github.com/trqminh/knowledge-distillation
https://github.com/trustairlab/mgtbench
https://github.com/tryolabs/luminoth
https://github.com/ttengwang/coco-caption
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COCO: A Platform for Comparing Continuous Optimizers in a Black-Box Setting
https://github.com/ttusar/coco-gbea
COCO: A Platform for Comparing Continuous Optimizers in a Black-Box Setting
https://github.com/tu184044109/BVQA_Benchmark
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https://github.com/tue-mps/vfm-uda
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출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.