Papers with Code 아카이브
Knowledge Distillation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 2
- 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/luanyunteng/pytorch-be-your-own-teacher
Be Your Own Teacher: Improve the Performance of Convolutional Neural Networks via Self Distillation
https://github.com/lucas-rbnt/bm-mae
https://github.com/luchris429/popjaxrl
POPGym: Benchmarking Partially Observable Reinforcement Learning
https://github.com/luchris429/s5rl
POPGym: Benchmarking Partially Observable Reinforcement Learning
https://github.com/lucidrains/marge-pytorch
Distilling Knowledge from Reader to Retriever for Question Answering
https://github.com/luciusssss/mlic-eval
MiLiC-Eval: Benchmarking Multilingual LLMs for China's Minority Languages
https://github.com/lukexyz/Deep-Lyrical-Genius
DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
https://github.com/lunaproject22/rpa
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/luoweizhou/coco-caption
https://github.com/lvapeab/coco-caption
https://github.com/lxa9867/R2VOS
$\text{R}^2$-Bench: Benchmarking the Robustness of Referring Perception Models under Perturbations
https://github.com/lyk19940625/MyRFBNet
https://github.com/lyon-nlp/mteb-french
https://github.com/machinelearninglifescience/poli
https://github.com/madrylab/pretraining-distribution-shift-robustness
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/magic-ai4med/medrbench
Quantifying the Reasoning Abilities of LLMs on Real-world Clinical Cases
https://github.com/magnificent1208/r3det-on-mmdetection
https://github.com/mahi97/XQMIX
https://github.com/mainaksingha01/applenet
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/mainaksingha01/odg-clip
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/majidAdibian77/ResGrad
FastSpeech 2: Fast and High-Quality End-to-End Text to Speech
https://github.com/malbergo/stochastic-interpolants
https://github.com/malerinc/fabricatio-rl
Towards Standardising Reinforcement Learning Approaches for Production Scheduling Problems
https://github.com/manishravula/yolov3_tiny_pruned
https://github.com/manncodes/dehazing-openvino
Knowledge distillation: A good teacher is patient and consistent
https://github.com/marcinknara/520-ML-DevToolKit
https://github.com/markliou/model_distillation
https://github.com/mathieuorhan/darknet
https://github.com/mattemartini/explainable-and-trustworthy-ai-project
ferret: a Framework for Benchmarking Explainers on Transformers
https://github.com/matyasbohacek/spoter
https://github.com/maumueller/ann-benchmarks-reproducibility
ANN-Benchmarks: A Benchmarking Tool for Approximate Nearest Neighbor Algorithms
https://github.com/max-planck-innovation-competition/sentence-transformers
Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation
https://github.com/mayubo2333/mmlongbench-doc
MMLongBench-Doc: Benchmarking Long-context Document Understanding with Visualizations
https://github.com/maziarg/PrivAttack-BCQ
https://github.com/mbzuai-nlp/llm-detectaive
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.