Papers with Code 아카이브
Data-free Knowledge Distillation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/extrality/iclr_naca_dataset_v0
공식 코드https://github.com/ezequieldlrosa/isles22
공식 코드ISLES 2022: A multi-center magnetic resonance imaging stroke lesion segmentation dataset
https://github.com/fabiopardo/tonic
공식 코드Tonic: A Deep Reinforcement Learning Library for Fast Prototyping and Benchmarking
https://github.com/fabrice-rossi/outlier-anomaly-detection
공식 코드https://github.com/facaraff/SOS
공식 코드https://github.com/facebookresearch/Multi-IF
공식 코드Multi-IF: Benchmarking LLMs on Multi-Turn and Multilingual Instructions Following
https://github.com/facebookresearch/audiobox-aesthetics
공식 코드Meta Audiobox Aesthetics: Unified Automatic Quality Assessment for Speech, Music, and Sound
https://github.com/facebookresearch/benchmarl
공식 코드https://github.com/facebookresearch/dig-in
공식 코드DIG In: Evaluating Disparities in Image Generations with Indicators for Geographic Diversity
https://github.com/facebookresearch/dynabench
공식 코드Dynatask: A Framework for Creating Dynamic AI Benchmark Tasks
https://github.com/facebookresearch/egotom
공식 코드EgoToM: Benchmarking Theory of Mind Reasoning from Egocentric Videos
https://github.com/facebookresearch/evalgim
공식 코드https://github.com/facebookresearch/exploring_exploration
공식 코드https://github.com/facebookresearch/fair_self_supervision_benchmark
공식 코드Scaling and Benchmarking Self-Supervised Visual Representation Learning
https://github.com/facebookresearch/fjd
공식 코드https://github.com/facebookresearch/habitat-sim
공식 코드https://github.com/facebookresearch/intphys2
공식 코드IntPhys 2: Benchmarking Intuitive Physics Understanding In Complex Synthetic Environments
https://github.com/facebookresearch/pearl
공식 코드https://github.com/facebookresearch/projectaria_tools
공식 코드Aria Digital Twin: A New Benchmark Dataset for Egocentric 3D Machine Perception
https://github.com/facebookresearch/pyrobot
공식 코드PyRobot: An Open-source Robotics Framework for Research and Benchmarking
https://github.com/facebookresearch/selavi
공식 코드Labelling unlabelled videos from scratch with multi-modal self-supervision
https://github.com/facebookresearch/tacto
공식 코드TACTO: A Fast, Flexible, and Open-source Simulator for High-Resolution Vision-based Tactile Sensors
https://github.com/facebookresearch/vizseq
공식 코드https://github.com/facebookresearch/wasp
공식 코드WASP: Benchmarking Web Agent Security Against Prompt Injection Attacks
https://github.com/fahim-sikder/fairx
공식 코드https://github.com/fair4hep/pfin4uqad
공식 코드https://github.com/fancyssc/step
공식 코드STEP: A Unified Spiking Transformer Evaluation Platform for Fair and Reproducible Benchmarking
https://github.com/farama-foundation/arcade-learning-environment
공식 코드https://github.com/farama-foundation/momaland
공식 코드MOMAland: A Set of Benchmarks for Multi-Objective Multi-Agent Reinforcement Learning
https://github.com/fau-lms/ncn_for_m2m
공식 코드Boosting Neural Image Compression for Machines Using Latent Space Masking
https://github.com/fchollet/ARC
공식 코드https://github.com/fedgraph/fedgraph
공식 코드FedGraph: A Research Library and Benchmark for Federated Graph Learning
https://github.com/fedml-ai/fedcv
공식 코드FedCV: A Federated Learning Framework for Diverse Computer Vision Tasks
https://github.com/feedzai/fifar-dataset
공식 코드https://github.com/feilumath/benchmark_TSC
공식 코드Benchmarking optimality of time series classification methods in distinguishing diffusions
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.