https://github.com/chenkang455/s-sdm
공식 코드SpikeReveal: Unlocking Temporal Sequences from Real Blurry Inputs with Spike Streams
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/chenshuang-zhang/imagenet_d
공식 코드ImageNet-D: Benchmarking Neural Network Robustness on Diffusion Synthetic Object
https://github.com/chenxiliu-hnu/timekd
공식 코드https://github.com/chenxin-dlut/seqtrackv2
공식 코드https://github.com/chenxing1999/recsys-benchmark
공식 코드A Thorough Performance Benchmarking on Lightweight Embedding-based Recommender Systems
https://github.com/chenyd7/pefd
공식 코드https://github.com/chenyi99/egoplan
공식 코드EgoPlan-Bench: Benchmarking Multimodal Large Language Models for Human-Level Planning
https://github.com/chenyuen0103/gender-bias
공식 코드Causally Testing Gender Bias in LLMs: A Case Study on Occupational Bias
https://github.com/chessbean1/gui-robust
공식 코드GUI-Robust: A Comprehensive Dataset for Testing GUI Agent Robustness in Real-World Anomalies
https://github.com/chiang-yuan/llamp
공식 코드https://github.com/chirag-w/qpsq-learning
공식 코드https://github.com/chiutaiyin/pca-knowledge-distillation
공식 코드https://github.com/chiyeunglaw/amr-evol
공식 코드https://github.com/choijunyong/orckd
공식 코드ORC: Network Group-based Knowledge Distillation using Online Role Change
https://github.com/chojw/genb
공식 코드https://github.com/choyingw/Voice2Mesh
공식 코드Voice2Mesh: Cross-Modal 3D Face Model Generation from Voices
https://github.com/chr26195/gkd
공식 코드Geometric Knowledge Distillation: Topology Compression for Graph Neural Networks
https://github.com/chris-n-self/large-scale-qml
공식 코드Quantum machine learning of large datasets using randomized measurements
https://github.com/chrisallenming/cross_category_video_highlight
공식 코드Cross-category Video Highlight Detection via Set-based Learning
https://github.com/christopher-br/cbrnet
공식 코드Using representation balancing to learn conditional-average dose responses from clustered data
https://github.com/christopherbrix/vnncomp2023_benchmarks
공식 코드https://github.com/christos42/Enhancing-Biomedical-Knowledge-Discovery-for-Diseases
공식 코드https://github.com/chumingqian/Model_Compression_For_YOLOV3-V4
공식 코드Group channel pruning and spatial attention distilling for object detection
https://github.com/cioppaanthony/multimodal-multiview-distillation
공식 코드Multimodal and multiview distillation for real-time player detection on a football field
https://github.com/cipriantruica/T2K2D2_Benchmark
공식 코드https://github.com/circlelzy/mtkd-cd
공식 코드https://github.com/citynet-at-git/Citynet-phase1
공식 코드CityNet: A Comprehensive Multi-Modal Urban Dataset for Advanced Research in Urban Computing
https://github.com/cityu-mlo/mitigate_architecture_overfitting
공식 코드Towards Mitigating Architecture Overfitting on Distilled Datasets
https://github.com/ckkelvinchan/realbasicvsr
공식 코드Investigating Tradeoffs in Real-World Video Super-Resolution
https://github.com/ckoorosh/robutext
공식 코드https://github.com/clarin-pl/lepiszcze
공식 코드This is the way: designing and compiling LEPISZCZE, a comprehensive NLP benchmark for Polish
https://github.com/claws-lab/mmsoc
공식 코드MM-Soc: Benchmarking Multimodal Large Language Models in Social Media Platforms
https://github.com/clementchadebec/benchmark_VAE
공식 코드Pythae: Unifying Generative Autoencoders in Python -- A Benchmarking Use Case
https://github.com/climsocana/tecb-de
공식 코드https://github.com/clinc/oos-eval
공식 코드An Evaluation Dataset for Intent Classification and Out-of-Scope Prediction
https://github.com/clinicalml/mimic_annotations
공식 코드Robust Benchmarking for Machine Learning of Clinical Entity Extraction
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.