https://github.com/vlar-group/unsupobjseg
공식 코드Benchmarking and Analysis of Unsupervised Object Segmentation from Real-world Single Images
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/voidism/L2KD
공식 코드https://github.com/vorausrobotik/voraus-ad-dataset
공식 코드The voraus-AD Dataset for Anomaly Detection in Robot Applications
https://github.com/vorobeevich/distillation-in-dg
공식 코드Weight Averaging Improves Knowledge Distillation under Domain Shift
https://github.com/vr18ub/court_view_generation
공식 코드One Law, Many Languages: Benchmarking Multilingual Legal Reasoning for Judicial Support
https://github.com/vslaykovsky/ai-sourcing-benchmark
공식 코드Evaluating AI Recruitment Sourcing Tools by Human Preference
https://github.com/vut-hfut/micro-action
공식 코드Benchmarking Micro-action Recognition: Dataset, Methods, and Applications
https://github.com/vwxyzjn/cleanrl
공식 코드CleanRL: High-quality Single-file Implementations of Deep Reinforcement Learning Algorithms
https://github.com/vyraun/hallucinations
공식 코드The Curious Case of Hallucinations in Neural Machine Translation
https://github.com/wabyking/qnn
공식 코드https://github.com/wad3birch/AutoBench-V
공식 코드AutoBench-V: Can Large Vision-Language Models Benchmark Themselves?
https://github.com/wadoodabdul/clinical_ner_benchmark
공식 코드https://github.com/walleclipse/reinforcement-learning-pulse-stacking
공식 코드An Optical Control Environment for Benchmarking Reinforcement Learning Algorithms
https://github.com/walledai/walledeval
공식 코드WalledEval: A Comprehensive Safety Evaluation Toolkit for Large Language Models
https://github.com/wang-jh18-svm/griffin
공식 코드Griffin: Aerial-Ground Cooperative Detection and Tracking Dataset and Benchmark
https://github.com/wang-ml-lab/multimodal-needle-in-a-haystack
공식 코드https://github.com/wang-research-lab/roz
공식 코드Benchmarking Zero-Shot Robustness of Multimodal Foundation Models: A Pilot Study
https://github.com/wangcongcong123/sta
공식 코드STA: Self-controlled Text Augmentation for Improving Text Classifications
https://github.com/wangjiarui153/AIGV-Assessor
공식 코드https://github.com/wangjiongw/freeman_api
공식 코드FreeMan: Towards Benchmarking 3D Human Pose Estimation under Real-World Conditions
https://github.com/wangjiyuan9/d4rd
공식 코드Digging into contrastive learning for robust depth estimation with diffusion models
https://github.com/wangkai930418/ERD
공식 코드Incremental Meta-Learning via Episodic Replay Distillation for Few-Shot Image Recognition
https://github.com/wangkai930418/dwopp_code
공식 코드https://github.com/wangpf3/consistent-cot-distillation
공식 코드https://github.com/wangsch10/v2x-dgpe
공식 코드V2X-DGPE: Addressing Domain Gaps and Pose Errors for Robust Collaborative 3D Object Detection
https://github.com/wangshaopu/prue
공식 코드https://github.com/wangwenhao0716/awesome-diffusion-replication
공식 코드Replication in Visual Diffusion Models: A Survey and Outlook
https://github.com/wangxb96/awesome-ai-on-the-edge
공식 코드Optimizing Edge AI: A Comprehensive Survey on Data, Model, and System Strategies
https://github.com/wangxiao5791509/Single_Object_Tracking_Paper_List
공식 코드Event Stream-based Visual Object Tracking: A High-Resolution Benchmark Dataset and A Novel Baseline
https://github.com/wangxiao5791509/VisEvent_SOT_Benchmark
공식 코드Event Stream-based Visual Object Tracking: A High-Resolution Benchmark Dataset and A Novel Baseline
https://github.com/wangyong1122/dtn
공식 코드Go From the General to the Particular: Multi-Domain Translation with Domain Transformation Networks
https://github.com/wangyueft/detr3d
공식 코드https://github.com/wangywust/robustre
공식 코드How Fragile is Relation Extraction under Entity Replacements?
https://github.com/wangyz1608/knowledge-distillation-via-nd
공식 코드Improving Knowledge Distillation via Regularizing Feature Norm and Direction
https://github.com/wangz3066/onlinedistillgcn
공식 코드Online Adversarial Knowledge Distillation for Graph Neural Networks
https://github.com/wangzl99/AdaptPrompt
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.