https://github.com/THU-LYJ-Lab/T3Bench
공식 코드T$^3$Bench: Benchmarking Current Progress in Text-to-3D Generation
Papers with Code 아카이브
Knowledge Distillation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 보존된 평가 기록
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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/THUDM/Android-Lab
공식 코드AndroidLab: Training and Systematic Benchmarking of Android Autonomous Agents
https://github.com/THUDM/CodeGeeX
공식 코드CodeGeeX: A Pre-Trained Model for Code Generation with Multilingual Benchmarking on HumanEval-X
https://github.com/THUDM/FewNLU
공식 코드FewNLU: Benchmarking State-of-the-Art Methods for Few-Shot Natural Language Understanding
https://github.com/TMMMU-Benchmark/evaluation
공식 코드VisTai: Benchmarking Vision-Language Models for Traditional Chinese in Taiwan
https://github.com/TalwalkarLab/leaf
공식 코드https://github.com/Tangshitao/Semi-supervised-Adaptive-Distillation
공식 코드Learning Efficient Detector with Semi-supervised Adaptive Distillation
https://github.com/TaoZhen1110/CUDRT_Benchmark
공식 코드Towards Reliable Detection of LLM-Generated Texts: A Comprehensive Evaluation Framework with CUDRT
https://github.com/TejasAnvekar/Mahalanobis-k-NN
공식 코드Mahalanobis k-NN: A Statistical Lens for Robust Point-Cloud Registrations
https://github.com/Tencent/TFace
공식 코드Evaluation-oriented Knowledge Distillation for Deep Face Recognition
https://github.com/Th0masLavigne/Porous_media_Generation_and_properties
공식 코드Synthetic Porous Microstructures: Automatic Design, Simulation, and Permeability Analysis
https://github.com/Thinklab-SJTU/Bench2Drive
공식 코드Bench2Drive: Towards Multi-Ability Benchmarking of Closed-Loop End-To-End Autonomous Driving
https://github.com/ThomasWestfechtel/SWG
공식 코드https://github.com/ThuongTNguyen/ALchemist
공식 코드https://github.com/TickleForce/ancient-greek-datasets
공식 코드Sentence Embedding Models for Ancient Greek Using Multilingual Knowledge Distillation
https://github.com/Tixierae/WECD
공식 코드https://github.com/TobyYang7/UCFE-Benchmark
공식 코드UCFE: A User-Centric Financial Expertise Benchmark for Large Language Models
https://github.com/Toloka/beemo
공식 코드https://github.com/Tomiinek/MultiWOZ_Evaluation
공식 코드https://github.com/TuAnh23/SciEx
공식 코드https://github.com/Twilight92z/Role-of-LLM
공식 코드Understanding the Role of LLMs in Multimodal Evaluation Benchmarks
https://github.com/UBCDingXin/cGAN-based_KD
공식 코드https://github.com/UKPLab/beir
공식 코드BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models
https://github.com/UKPLab/sentence-transformers
공식 코드Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation
https://github.com/UMBCvision/SSL-Backdoor
공식 코드https://github.com/USC-Melady/Benchmarking_DL_MIMICIII
공식 코드Benchmark of Deep Learning Models on Large Healthcare MIMIC Datasets
https://github.com/UlrikFriisJensen/CHILI
공식 코드https://github.com/VDIGPKU/ContinualContrastiveLearning
공식 코드https://github.com/VICO-UoE/KD4MTL
공식 코드https://github.com/VITA-Group/KD-ticket
공식 코드https://github.com/VITA-Group/LightGaussian
공식 코드LightGaussian: Unbounded 3D Gaussian Compression with 15x Reduction and 200+ FPS
https://github.com/VITA-Group/Nasty-Teacher
공식 코드Undistillable: Making A Nasty Teacher That CANNOT teach students
https://github.com/VITA-Group/Open-L2O
공식 코드https://github.com/VITA-Group/UVC
공식 코드https://github.com/VainF/Data-Free-Adversarial-Distillation
공식 코드https://github.com/VirtualEmbryo/ZAugNet
공식 코드Self-Supervised Z-Slice Augmentation for 3D Bio-Imaging via Knowledge Distillation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.