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/flagopen/flaginstruct
Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks
https://github.com/flaudem/ProjetDeepLearnig_IAS
https://github.com/flexible-fl/flex-nlp
DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
https://github.com/floatingstarZ/loc_cls_exp
https://github.com/flyingTan/QuantMmdetection
https://github.com/flytocc/PaddleClas
https://github.com/formal-verification-research/NJSMA
Technical Report on the CleverHans v2.1.0 Adversarial Examples Library
https://github.com/frankaging/Causal-Distill
DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
https://github.com/franknb/Text-Summarization
https://github.com/frgfm/torch-cam
Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks
https://github.com/fschmid56/efficientat_hear
Efficient Large-scale Audio Tagging via Transformer-to-CNN Knowledge Distillation
https://github.com/ftorres11/saliencysense
Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks
https://github.com/fudandisc/reform-eval
MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models
https://github.com/fujianhai/mmdet-king
https://github.com/fznsakib/great-ape-behaviour-detector
https://github.com/g-jozsef/sampling-framework
Ranking and benchmarking framework for sampling algorithms on synthetic data streams
https://github.com/g-u-n/pycil
Maintaining Discrimination and Fairness in Class Incremental Learning
https://github.com/ga642381/FastSpeech2
FastSpeech 2: Fast and High-Quality End-to-End Text to Speech
https://github.com/gabrieldewetLM/ml-demo
Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms
https://github.com/gabrielsantosrv/coco-caption
https://github.com/galaxycong/hpmdubbing
FastSpeech 2: Fast and High-Quality End-to-End Text to Speech
https://github.com/gallegi/T4E_MICCAI_BrainTumor
https://github.com/gasserelbanna/serab-byols
SERAB: A multi-lingual benchmark for speech emotion recognition
https://github.com/gaurav67890/mmdetection
https://github.com/ge-xing/segmamba
https://github.com/generation21/generation6011
https://github.com/geoffwoollard/learn_cryoem_math
https://github.com/geometric-intelligence/topobenchmark
TopoBench: A Framework for Benchmarking Topological Deep Learning
https://github.com/gfursin/browser-extension-for-reproducible-research
https://github.com/gfursin/ck-browser-addon
https://github.com/gfursin/ck-browser-addon-firefox
https://github.com/ghadahamed/darknet
https://github.com/ghaniskn/GCorp-Darknet
https://github.com/giantseaweed/decree
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/gingkg/smac
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.