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Benchmarking
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/decisionintelligence/catch
TAB: Unified Benchmarking of Time Series Anomaly Detection Methods
https://github.com/deep-privacy/Voice-Privacy-Challenge-2020
https://github.com/deeplearning-wisc/hypo
Benchmarking Neural Network Robustness to Common Corruptions and Perturbations
https://github.com/deepmind/optax
Benchopt: Reproducible, efficient and collaborative optimization benchmarks
https://github.com/deepmodeling/Uni-Mol
Highly Accurate Quantum Chemical Property Prediction with Uni-Mol+
https://github.com/dereyly/mmdet_sota
https://github.com/detecting-cheapfakes/detecting-cheapfakes-code
COSMOS: Catching Out-of-Context Misinformation with Self-Supervised Learning
https://github.com/dfcf93/MSMARCO
MS MARCO: A Human Generated MAchine Reading COmprehension Dataset
https://github.com/dhansmair/flamingo-mini
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/dhifoster/msc-birkbeck-ml-cwk
Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms
https://github.com/dimahwang88/mmdetection-multigpu-train
https://github.com/dmachlanski/benchpress-dm
Benchpress: A Scalable and Versatile Workflow for Benchmarking Structure Learning Algorithms
https://github.com/dmitrykazhdan/MEME-RNN-XAI
Multitask learning and benchmarking with clinical time series data
https://github.com/dogyoonlee/RSMix
Benchmarking Robustness of 3D Point Cloud Recognition Against Common Corruptions
https://github.com/dogyoonlee/RSMix-official
Benchmarking Robustness of 3D Point Cloud Recognition Against Common Corruptions
https://github.com/donghang941114/mmdetection_my
https://github.com/dortsur/heavywater_simplexwater
WaterBench: Towards Holistic Evaluation of Watermarks for Large Language Models
https://github.com/ds-academy/TadGAN
TadGAN: Time Series Anomaly Detection Using Generative Adversarial Networks
https://github.com/dtabas/epymarl
https://github.com/dthiagarajan/ptb-xl-research
Deep Learning for ECG Analysis: Benchmarks and Insights from PTB-XL
https://github.com/dti-research/SenseAct
Benchmarking Reinforcement Learning Algorithms on Real-World Robots
https://github.com/ducha-aiki/pydegensac
Image Matching across Wide Baselines: From Paper to Practice
https://github.com/ducha-aiki/pymagsac
Image Matching across Wide Baselines: From Paper to Practice
https://github.com/dunni3/keypoint-diffusion
Benchmarking Generated Poses: How Rational is Structure-based Drug Design with Generative Models?
https://github.com/edgarjimenez1996/-fashion-mnist
Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms
https://github.com/edisonlxy/detectiontool
https://github.com/edithal-14/A-Deep-Neural-Solution-To-Document-Level-Novelty-Detection-COLING-2018-
TAP-DLND 1.0 : A Corpus for Document Level Novelty Detection
https://github.com/eify/open_clip
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/elites2k19/prism-attack
Technical Report on the CleverHans v2.1.0 Adversarial Examples Library
https://github.com/elliottd/compareImageDescriptionMeasures
https://github.com/elsalmi/LendingClub
https://github.com/emanuelevivoli/CoMix
Comics Datasets Framework: Mix of Comics datasets for detection benchmarking
https://github.com/emi-group/evox
EvoRL: A GPU-accelerated Framework for Evolutionary Reinforcement Learning
https://github.com/emory-irlab/argumentation-rag
ConQRet: Benchmarking Fine-Grained Evaluation of Retrieval Augmented Argumentation with LLM Judges
https://github.com/epfl-dlab/multilingual-entity-insertion
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.