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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/p517332051/GAN-Knowledge-Distillation-SSD
Distilling Object Detectors with Fine-grained Feature Imitation
https://github.com/paddlepaddle/edl
https://github.com/pamela-project/slambench
Introducing SLAMBench, a performance and accuracy benchmarking methodology for SLAM
https://github.com/paolanu/BERT_epitope
Well-Read Students Learn Better: On the Importance of Pre-training Compact Models
https://github.com/pasrichashivam/Convnets_Grad-CAM_Grad-CAM-PlusPlus_Keras_tensorflow-2
Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks
https://github.com/patelmiteshn/darknet
https://github.com/patsp/coco
COCO: A Platform for Comparing Continuous Optimizers in a Black-Box Setting
https://github.com/pbelcak/ultrafastbert
https://github.com/pearl-utexas/sope
Empirical Study of Off-Policy Policy Evaluation for Reinforcement Learning
https://github.com/peizeli96/Object-attribute-matters-in-visual-question-answering
https://github.com/pencilzhang/LFNet-light-field-saliency-net
Light Field Saliency Detection with Deep Convolutional Networks
https://github.com/pengrenlai/RetinaNet-tensorflow
https://github.com/peteanderson80/coco-caption
https://github.com/petrum01/Capstone_project_object_detection
https://github.com/petteriTeikari/vesselNN
https://github.com/philipp238/parfam
Contemporary Symbolic Regression Methods and their Relative Performance
https://github.com/philschmid/knowledge-distillation-transformers-pytorch-sagemaker
DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
https://github.com/phixion/phixion
Scaling of Search and Learning: A Roadmap to Reproduce o1 from Reinforcement Learning Perspective
https://github.com/pierluigiferrari/ssd_keras
https://github.com/pingheng001/Cnn-Bert
Well-Read Students Learn Better: On the Importance of Pre-training Compact Models
https://github.com/pminhtam/DBSN_pytorch
https://github.com/pohlinwei/AComPianist
Building a Large Scale Dataset for Image Emotion Recognition: The Fine Print and The Benchmark
https://github.com/pointcept/sampart3d
https://github.com/pokaxpoka/RAD_state_aug
https://github.com/pokaxpoka/rad_openaigym
https://github.com/ppokhare/Handling-Imbalanced-Dataset-for-classification-task
https://github.com/pra-dan/Worth-Reading-ML-Papers
https://github.com/prabhupad26/100daysofML
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/pralab/secml
Technical Report on the CleverHans v2.1.0 Adversarial Examples Library
https://github.com/princeton-nlp/helmet
MS MARCO: A Human Generated MAchine Reading COmprehension Dataset
https://github.com/projeto-de-algoritmos/Grafos1_Joao_Lucas_Leonardo_Miranda
Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation
https://github.com/protagolabs/odyssey-math
https://github.com/pseulki/rococo
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/pulkit-khandelwal/picsl-ex-vivo-segmentation
https://github.com/pwc-1/Paper-10/tree/main/stablelm
StableToolBench: Towards Stable Large-Scale Benchmarking on Tool Learning of Large Language Models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.