https://github.com/shashidhar100/Comparison-of-gradcam-and-gradcam_plus_plus_keras
Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks
Papers with Code 아카이브
Knowledge Distillation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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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/shashigharti/secure-privateai-scholarship-challenge
https://github.com/shawnyuen/DataAugmentationPaperCollection
https://github.com/shengcailiao/QAConv
Is Synthetic Dataset Reliable for Benchmarking Generalizable Person Re-Identification?
https://github.com/shijiel2/cleverhans
Technical Report on the CleverHans v2.1.0 Adversarial Examples Library
https://github.com/shinome551/select_with_distillation
https://github.com/shinya7y/UniverseNet
https://github.com/shivammehta25/BetterFastSpeech2
FastSpeech 2: Fast and High-Quality End-to-End Text to Speech
https://github.com/shivammehta25/clip
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/shkarupa-alex/tfclip
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/showlab/data-centric.vlp
Beyond neural scaling laws: beating power law scaling via data pruning
https://github.com/showlab/datacentric.vlp
Beyond neural scaling laws: beating power law scaling via data pruning
https://github.com/shriramsb/Distilling-the-Knowledge-in-a-Neural-Network
https://github.com/shunk031/simple-aesthetics-predictor
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/si0wang/vicrit
SoTA with Less: MCTS-Guided Sample Selection for Data-Efficient Visual Reasoning Self-Improvement
https://github.com/sicara/easy-few-shot-learning
https://github.com/sicara/tf-explain
Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks
https://github.com/sidify/resnet_focal_loss
https://github.com/sihaoevery/TaT-seg
https://github.com/silist/mmdet_dev
https://github.com/simonlevine/11785-project
https://github.com/simonlevine/clinical-longformer
https://github.com/simsimiSION/pymarl-algorithm-extension-via-starcraft
https://github.com/sincerass/mvlpt
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/sisl/event-driven-rllab
Benchmarking Deep Reinforcement Learning for Continuous Control
https://github.com/sisl/gail-driver
Benchmarking Deep Reinforcement Learning for Continuous Control
https://github.com/sithu31296/simple-object-tracking
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/siyi-wind/tip
MDViT: Multi-domain Vision Transformer for Small Medical Image Segmentation Datasets
https://github.com/sjduan/LeHDC
https://github.com/sjtu-dmtai/onlinetsf
Dark Experience for General Continual Learning: a Strong, Simple Baseline
https://github.com/skbadhsm/darknet2
https://github.com/skylerlinn/understanding-the-ranking-loss
BARS-CTR: Open Benchmarking for Click-Through Rate Prediction
https://github.com/skyoux/mokd
Multi-Mode Online Knowledge Distillation for Self-Supervised Visual Representation Learning
https://github.com/smartadpole/spider
AlphaPose: Whole-Body Regional Multi-Person Pose Estimation and Tracking in Real-Time
https://github.com/smurthy2020/fashion
Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms
https://github.com/snel-repo/expressive-latent-dynamics-paper
Chaos as an interpretable benchmark for forecasting and data-driven modelling
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.