Papers with Code 아카이브
Knowledge Distillation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
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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.
https://github.com/aws-samples/amazon-sagemaker-spleen-image-segmentation
https://github.com/axiomlab/Cable
MS MARCO: A Human Generated MAchine Reading COmprehension Dataset
https://github.com/ayeshakhtar209/fashion-mnist
Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms
https://github.com/ayushpalak/darknet_cokebottles
https://github.com/ayushpalak/darknet_multi_cokebottles
https://github.com/azibit/detection
https://github.com/azshue/TPT
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/b4shy/Medical
https://github.com/baai-zlab/coig
Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks
https://github.com/balakg/transects-eccv2020
Towards causal benchmarking of bias in face analysis algorithms
https://github.com/balamurugan0714/TensorFlow
Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms
https://github.com/bald6354/edncnn
https://github.com/bartn8/vppstereo
https://github.com/basf/chemteb
https://github.com/baskargroup/Arboretum
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/baskargroup/biotrove
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/beapc18/AffordanceNet
Recognizing Object Affordances to Support Scene Reasoning for Manipulation Tasks
https://github.com/beibuwandeluori/DFGC_Detection
https://github.com/beir-cellar/beir
BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models
https://github.com/benediktstroebl/inference-scaling-limits
Beyond Correctness: Benchmarking Multi-dimensional Code Generation for Large Language Models
https://github.com/benihime91/pytorch_retinanet
https://github.com/betterzhou/AAGNN
BOND: Benchmarking Unsupervised Outlier Node Detection on Static Attributed Graphs
https://github.com/bharatsau/Darknet2
https://github.com/bharatsush/object_detection
https://github.com/bigscience-workshop/biomedical
https://github.com/binhdv92/darknet
https://github.com/binhdv92/darknet_kriyeng
https://github.com/bioscan-ml/bioscan-5m
An Empirical Study into Clustering of Unseen Datasets with Self-Supervised Encoders
https://github.com/birkhoffkiki/gpfm
Towards A Generalizable Pathology Foundation Model via Unified Knowledge Distillation
https://github.com/bkmi/cnre
https://github.com/bm-k/kosentencebert-skt
Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation
https://github.com/bmw-group-quantum/quark
Application-Oriented Benchmarking of Quantum Generative Learning Using QUARK
https://github.com/bobby20180331/darknet_pycharm
https://github.com/borisdayma/clip-jax
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/boyellow/adaad
LTD: Low Temperature Distillation for Robust Adversarial Training
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.