https://github.com/rinnakk/japanese-clip
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/rishikksh20/FastSpeech2
FastSpeech 2: Fast and High-Quality End-to-End Text to Speech
https://github.com/rishikksh20/LightSpeech
FastSpeech 2: Fast and High-Quality End-to-End Text to Speech
https://github.com/rjagerman/sigir2019-user-interactions
https://github.com/rll/rllab
Benchmarking Deep Reinforcement Learning for Continuous Control
https://github.com/rlworkgroup/garage
Benchmarking Deep Reinforcement Learning for Continuous Control
https://github.com/rnirdhar/yoloMultiClass
https://github.com/rnirdhar/yoloTestOneClass
https://github.com/robertjkeck2/EmoNet
https://github.com/robertjkeck2/EmoTe
https://github.com/robertjkeck2/pwdl
https://github.com/robertsenps/data
Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms
https://github.com/robfiras/ls-iq
LocoMuJoCo: A Comprehensive Imitation Learning Benchmark for Locomotion
https://github.com/robflynnyh/long-context-asr
ESB: A Benchmark For Multi-Domain End-to-End Speech Recognition
https://github.com/roedoejet/fastspeech2
FastSpeech 2: Fast and High-Quality End-to-End Text to Speech
https://github.com/roedoejet/fastspeech2_acl2022_reproducibility
FastSpeech 2: Fast and High-Quality End-to-End Text to Speech
https://github.com/rohan400/PepperQA
Well-Read Students Learn Better: On the Importance of Pre-training Compact Models
https://github.com/rohban-lab/Knowledge_Distillation_AD
Multiresolution Knowledge Distillation for Anomaly Detection
https://github.com/rohin-dasari/Tensorflow-Knowledge-Distillation
https://github.com/royjames/room-impulse-responses
dEchorate: a Calibrated Room Impulse Response Database for Echo-aware Signal Processing
https://github.com/rozendetr/mmdetection_copy
https://github.com/ruanchaves/BLINK
Scalable Zero-shot Entity Linking with Dense Entity Retrieval
https://github.com/ruinmessi/RFBNet
https://github.com/ruotianluo/coco-caption
https://github.com/russellmendonca/maesn_suite
Benchmarking Deep Reinforcement Learning for Continuous Control
https://github.com/rwaithaka/testdrive2
Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms
https://github.com/rwightman/pytorch-image-models
TinyViT: Fast Pretraining Distillation for Small Vision Transformers
https://github.com/rybread1/stack-overflow-question-classification
DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
https://github.com/ryojitanabe/APL
COCO: A Platform for Comparing Continuous Optimizers in a Black-Box Setting
https://github.com/ryojitanabe/ela_drframework
COCO: A Platform for Comparing Continuous Optimizers in a Black-Box Setting
https://github.com/ryojitanabe/tpb
COCO: A Platform for Comparing Continuous Optimizers in a Black-Box Setting
https://github.com/ryubidragonfire/mydarknet
https://github.com/s-a-malik/multi-few
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/s-gupta/map-plan-baseline
https://github.com/saber2011/darknet
https://github.com/saccharomycetes/text-based-traffic-understanding
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.