https://github.com/taited/clip-score
Learning Transferable Visual Models From Natural Language Supervision
Papers with Code 아카이브
Knowledge Distillation
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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/tajwarfahim/proactive_interventions
Autonomous Reinforcement Learning: Formalism and Benchmarking
https://github.com/takashiishida/irreducible
Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms
https://github.com/tallysprado/myDarknet
https://github.com/tamerthamoqa/3D-mri-brain-tumour-image-segmentation-medical-decathlon-tensorflow
https://github.com/tanny411/llm-reliability-and-consistency-evaluation
TruthEval: A Dataset to Evaluate LLM Truthfulness and Reliability
https://github.com/tartunlp/transformertts
FastSpeech 2: Fast and High-Quality End-to-End Text to Speech
https://github.com/tarun-munjal/mmdetection
https://github.com/tawald/nnunet
nnU-Net Revisited: A Call for Rigorous Validation in 3D Medical Image Segmentation
https://github.com/taylorhawks/RNN-music-recommender
https://github.com/taylorhawks/Recommender
https://github.com/tchebonenko/Automated-Topic_Modeling-and-NER
DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
https://github.com/tcl9876/denoising_synthesis
Knowledge Distillation in Iterative Generative Models for Improved Sampling Speed
https://github.com/tedmoskovitz/DOPE
https://github.com/tedmoskovitz/TOP
https://github.com/teemoeric/projet
https://github.com/temibabs/data-free-adversarial-distillation
https://github.com/tensorflow/models
https://github.com/tensorflow/models/tree/master/research/delf
Revisiting Oxford and Paris: Large-Scale Image Retrieval Benchmarking
https://github.com/terrierteam/pyterrier_dr
Distilling Dense Representations for Ranking using Tightly-Coupled Teachers
https://github.com/testingautomated-usi/fashion-mnist-c
https://github.com/text-to-audio/make-an-audio
ProDiff: Progressive Fast Diffusion Model For High-Quality Text-to-Speech
https://github.com/thangvubk/SCNet
https://github.com/thaonguyen19/modeldistillation-pytorch
https://github.com/the-Quert/iNLPfun
Well-Read Students Learn Better: On the Importance of Pre-training Compact Models
https://github.com/the-super-toys/glimpse-models
Faster gaze prediction with dense networks and Fisher pruning
https://github.com/theQuert/inlpfun
Well-Read Students Learn Better: On the Importance of Pre-training Compact Models
https://github.com/thecodemasterk/BERT
Well-Read Students Learn Better: On the Importance of Pre-training Compact Models
https://github.com/thejungwon/gc-fed
Preservation of the Global Knowledge by Not-True Distillation in Federated Learning
https://github.com/thousandvoices/ok_ml_cup
Well-Read Students Learn Better: On the Importance of Pre-training Compact Models
https://github.com/thu-ml/realsafe
RobFR: Benchmarking Adversarial Robustness on Face Recognition
https://github.com/thudm/codegeex2
CodeGeeX: A Pre-Trained Model for Code Generation with Multilingual Benchmarking on HumanEval-X
https://github.com/thuiar/textoir
An Evaluation Dataset for Intent Classification and Out-of-Scope Prediction
https://github.com/thuiar/textoir-demo
An Evaluation Dataset for Intent Classification and Out-of-Scope Prediction
https://github.com/thunlp/elle
https://github.com/thxsxth/POMDP_RLSepsis
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.