Papers with Code 아카이브
Knowledge Distillation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 평가표
- 11
- 보존된 평가 기록
- 90
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.
https://github.com/leffff/graphormer-pyg
Benchmarking Graphormer on Large-Scale Molecular Modeling Datasets
https://github.com/lenscloth/RKD
https://github.com/leo-liuzy/codeupdatearena
CodeUpdateArena: Benchmarking Knowledge Editing on API Updates
https://github.com/leolee99/CLIP_ITM
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/leondgarse/keras_cv_attention_models/tree/main/keras_cv_attention_models/beit
BEiT v2: Masked Image Modeling with Vector-Quantized Visual Tokenizers
https://github.com/lewandofskee/mambaad
Real-IAD: A Real-World Multi-View Dataset for Benchmarking Versatile Industrial Anomaly Detection
https://github.com/lhcezx/Graph-FPN
https://github.com/lheim/TFLM-Evaluation
Measuring what Really Matters: Optimizing Neural Networks for TinyML
https://github.com/lheim/TFLM-Toolchain
Measuring what Really Matters: Optimizing Neural Networks for TinyML
https://github.com/lhwcv/mmdet
https://github.com/li-yibing/oceannet
https://github.com/liangcaisu/stem
BARS-CTR: Open Benchmarking for Click-Through Rate Prediction
https://github.com/lifelong-robot-learning/libero
LIBERO: Benchmarking Knowledge Transfer for Lifelong Robot Learning
https://github.com/lifu-tu/BenchmarkingApproximateInference
Benchmarking Approximate Inference Methods for Neural Structured Prediction
https://github.com/lijiaman/egoego_release
https://github.com/liketheflower/jimmy_dgl
https://github.com/lilygeorgescu/AED-SSMTL
Anomaly Detection in Video via Self-Supervised and Multi-Task Learning
https://github.com/ling-pan/res
https://github.com/lingyucai/Distilling-the-Knowledge-in-a-Neural-Network
https://github.com/linjieli222/hero_video_feature_extractor
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/linzhiqiu/t2i_metrics
GenAI-Bench: Evaluating and Improving Compositional Text-to-Visual Generation
https://github.com/lippman1125/mmdetection_lqy
https://github.com/lishenghui/ARFL
https://github.com/lisssse14/Grad_CAM_PLUS_PLUS
Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks
https://github.com/littletomatodonkey/code_scipts
Rethinking Soft Labels for Knowledge Distillation: A Bias-Variance Tradeoff Perspective
https://github.com/liyemei/Deformable-active-convolutional
https://github.com/lizhe960118/CenterNet
https://github.com/lizonghang/leaf-mx
https://github.com/ljjyxz123/mmdetection
https://github.com/llm-council/llm-council
Language Model Council: Democratically Benchmarking Foundation Models on Highly Subjective Tasks
https://github.com/llsouder/screen-ocr
https://github.com/llx666/git_demo
Well-Read Students Learn Better: On the Importance of Pre-training Compact Models
https://github.com/loveheaven/scann
Accelerating Large-Scale Inference with Anisotropic Vector Quantization
https://github.com/lsj2408/Transformer-M
Benchmarking Graphormer on Large-Scale Molecular Modeling Datasets
https://github.com/ltnghia/ANet
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.