https://github.com/harshit17chaudhary/SML_assignment_1
Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms
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/hasaniqbal777/openfactcheck
https://github.com/haysacks/spot-the-coin
https://github.com/hehehemin/DotaDetection
https://github.com/hhuaibo/darknet
https://github.com/hisiter97/darknet
https://github.com/hitplz/dstrformer
https://github.com/hkuedl/Task-Oriented-Imputation
https://github.com/hkunlp/symgen
ZeroGen: Efficient Zero-shot Learning via Dataset Generation
https://github.com/hkust-aerial-robotics/epsilon
Safe Trajectory Generation for Complex Urban Environments Using Spatio-temporal Semantic Corridor
https://github.com/hmomin/CORL-compare
CORL: Research-oriented Deep Offline Reinforcement Learning Library
https://github.com/hms-dbmi/chief
A deep learning framework for efficient pathology image analysis
https://github.com/hojjat-mokhtarabadi/promptsource
Multitask Prompted Training Enables Zero-Shot Task Generalization
https://github.com/howuhh/sac-n-jax
CORL: Research-oriented Deep Offline Reinforcement Learning Library
https://github.com/huawei-noah/DAFL
https://github.com/huawei-noah/Data-Efficient-Model-Compression
https://github.com/huawei-noah/KD-NLP/tree/main/Annealing_KD
https://github.com/hubtru/LTBoost
TFB: Towards Comprehensive and Fair Benchmarking of Time Series Forecasting Methods
https://github.com/huckiyang/MOT-Kuzushiji-Fashion-Video
Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms
https://github.com/huggingface/node-question-answering
DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
https://github.com/huggingface/open_asr_leaderboard
ESB: A Benchmark For Multi-Domain End-to-End Speech Recognition
https://github.com/huggingface/tflite-android-transformers
DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
https://github.com/huggingface/transformers-bloom-inference
ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers
https://github.com/huhengtong/UKD_CVPR2020
Creating Something from Nothing: Unsupervised Knowledge Distillation for Cross-Modal Hashing
https://github.com/hukefei/deep-person-reid-master
Torchreid: A Library for Deep Learning Person Re-Identification in Pytorch
https://github.com/hukefei/taobaolive
https://github.com/hunto/image_classification_sota
https://github.com/hustvl/mimdet
Benchmarking Detection Transfer Learning with Vision Transformers
https://github.com/huutuongtu/skd-ctc
Guiding Frame-Level CTC Alignments Using Self-knowledge Distillation
https://github.com/huytranvan2010/RetinaNet-Understanding
https://github.com/hweersot/darknet-custom
https://github.com/hyang0129/foodclassapp
https://github.com/hyintell/citb
Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks
https://github.com/hysshy/hyv9_mmdetection
https://github.com/hysts/pytorch_D-RISE
https://github.com/hz-ants/mmdetection
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.