https://github.com/wad3birch/AutoBench-V
공식 코드AutoBench-V: Can Large Vision-Language Models Benchmark Themselves?
Papers with Code 아카이브
Benchmarking
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
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/wangwenhao0716/awesome-diffusion-replication
공식 코드Replication in Visual Diffusion Models: A Survey and Outlook
https://github.com/wangywust/robustre
공식 코드How Fragile is Relation Extraction under Entity Replacements?
https://github.com/wanliyoung/collapse-in-model-editing
공식 코드The Butterfly Effect of Model Editing: Few Edits Can Trigger Large Language Models Collapse
https://github.com/watermeloncq/drl-dynamic-optimization-portfolio-SB3py37
공식 코드https://github.com/wayveai/wayve_scenes
공식 코드WayveScenes101: A Dataset and Benchmark for Novel View Synthesis in Autonomous Driving
https://github.com/wbkit/scribbles4all
공식 코드Scribbles for All: Benchmarking Scribble Supervised Segmentation Across Datasets
https://github.com/wchao0601/m4-sar
공식 코드https://github.com/wcx21/Social-Tasks-in-Sandbox-Simulation
공식 코드Towards Objectively Benchmarking Social Intelligence for Language Agents at Action Level
https://github.com/wdika/atommic
공식 코드https://github.com/wearepal/okapi
공식 코드Okapi: Generalising Better by Making Statistical Matches Match
https://github.com/webpai/interaction2code
공식 코드https://github.com/weipingfu/qgeval
공식 코드QGEval: Benchmarking Multi-dimensional Evaluation for Question Generation
https://github.com/weixi-feng/tc-bench
공식 코드TC-Bench: Benchmarking Temporal Compositionality in Text-to-Video and Image-to-Video Generation
https://github.com/weixin-liang/metashift
공식 코드https://github.com/weiyuchen1999/LGISS
공식 코드https://github.com/weizhenliubioinform/fish-phenotype-detect
공식 코드https://github.com/wenhuchen/time-sensitive-qa
공식 코드https://github.com/wenjiedu/awesome_imputation
공식 코드https://github.com/wenzhengzhang/entqa
공식 코드https://github.com/werkaaa/iscm
공식 코드https://github.com/westlake-repl/microlens
공식 코드A Content-Driven Micro-Video Recommendation Dataset at Scale
https://github.com/westlake-repl/pixelrec
공식 코드An Image Dataset for Benchmarking Recommender Systems with Raw Pixels
https://github.com/whopriyam/Benchmarking-Differential-Privacy-and-Federated-Learning-for-BERT-Models
공식 코드Benchmarking Differential Privacy and Federated Learning for BERT Models
https://github.com/wildltr/ptranking
공식 코드PT-Ranking: A Benchmarking Platform for Neural Learning-to-Rank
https://github.com/williamcorrias/maya-password-benchmarking
공식 코드MAYA: Addressing Inconsistencies in Generative Password Guessing through a Unified Benchmark
https://github.com/williamgilpin/dysts
공식 코드Chaos 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.