https://github.com/twobooks/intro-aws-training
DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
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/txyugood/Knowledge_Distillation_AD_Paddle
Multiresolution Knowledge Distillation for Anomaly Detection
https://github.com/tylin/coco-caption
https://github.com/ubc-tea/DESA
Overcoming Data and Model Heterogeneities in Decentralized Federated Learning via Synthetic Anchors
https://github.com/uber/ludwig
Personalized Benchmarking with the Ludwig Benchmarking Toolkit
https://github.com/udaydutt12/Neural-Nets
Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms
https://github.com/uds-lsv/menyo-20k_mt
The Effect of Domain and Diacritics in Yorùbá-English Neural Machine Translation
https://github.com/uicdice/imm-language-modeling
Induced Model Matching: How Restricted Models Can Help Larger Ones
https://github.com/uicdice/imm-reinforce
Induced Model Matching: How Restricted Models Can Help Larger Ones
https://github.com/ukplab/5pils
HintsOfTruth: A Multimodal Checkworthiness Detection Dataset with Real and Synthetic Claims
https://github.com/ukplab/arxiv2022-context-injection-stance
Multitask Prompted Training Enables Zero-Shot Task Generalization
https://github.com/unconst/MACH
https://github.com/unilight/s3prl-vc
S3PRL-VC: Open-source Voice Conversion Framework with Self-supervised Speech Representations
https://github.com/unsky/RetinaNet
https://github.com/unsky/focal-loss
https://github.com/unveilx/slm-od-ml-comparison
Battle of the Backbones: A Large-Scale Comparison of Pretrained Models across Computer Vision Tasks
https://github.com/urbaneman/mmdetection_mobilenet
https://github.com/v1an1/gradcamplusplus-cats-v-dogs
Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks
https://github.com/valeoai/QuEST
https://github.com/vantupham/darknet
https://github.com/vaseline555/Federated-Learning-in-PyTorch
https://github.com/vcowwy/paddle-grad-cam
Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks
https://github.com/veikkahonkanen/Seizurenet-replica
SeizureNet: Multi-Spectral Deep Feature Learning for Seizure Type Classification
https://github.com/vico-uoe/universalrepresentations
https://github.com/viethungluu/OCRetina
https://github.com/vikashchalla123/mmdet_b1.2.0_fork
https://github.com/vinuni-vishc/feddct
Group Knowledge Transfer: Federated Learning of Large CNNs at the Edge
https://github.com/violet-zct/fairseq-detect-hallucination
Go From the General to the Particular: Multi-Domain Translation with Domain Transformation Networks
https://github.com/vislearn/multi-matching-optimization
https://github.com/vita-group/gan-slimming
AutoGAN-Distiller: Searching to Compress Generative Adversarial Networks
https://github.com/vividwalker/Multi-Head-Rag
https://github.com/vkartik97/habitat-challenge-two-docker
https://github.com/vlomonaco/US-TransportationMode
Custom Dual Transportation Mode Detection by Smartphone Devices Exploiting Sensor Diversity
https://github.com/vrama91/cider
https://github.com/vrama91/coco-caption
https://github.com/vrunm/text-classification-financial-phrase-bank
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.