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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/SkelterLabsInc/JaQuAD
공식 코드JaQuAD: Japanese Question Answering Dataset for Machine Reading Comprehension
https://github.com/SkoltechAI/fewbit
공식 코드Few-Bit Backward: Quantized Gradients of Activation Functions for Memory Footprint Reduction
https://github.com/So-Cool/bLIMEy
공식 코드https://github.com/SoheilaHatami/Face-Mask-Detection
공식 코드Wearing face mask detection using deep learning through COVID-19 pandemic
https://github.com/Sompote/YOLO9tr
공식 코드https://github.com/SonyResearch/RAW-Diffusion
공식 코드RAW-Diffusion: RGB-Guided Diffusion Models for High-Fidelity RAW Image Generation
https://github.com/SonyResearch/diffvox
공식 코드Improving Inference-Time Optimisation for Vocal Effects Style Transfer with a Gaussian Prior
https://github.com/Sophie-Xu/Pose_DomainAdaption
공식 코드Alleviating Human-level Shift : A Robust Domain Adaptation Method for Multi-person Pose Estimation
https://github.com/Spenhouet/automated-deep-photo-style-transfer
공식 코드https://github.com/StarsThu2016/ApproxDet
공식 코드ApproxDet: Content and Contention-Aware Approximate Object Detection for Mobiles
https://github.com/StevenGrove/DynamicHead
공식 코드https://github.com/StevenGrove/LearnableTreeFilterV2
공식 코드Rethinking Learnable Tree Filter for Generic Feature Transform
https://github.com/StiphyJay/FastPillars
공식 코드https://github.com/StonyBrookNLP/PerSenT
공식 코드https://github.com/SunYM2020/UA-CMDet
공식 코드Drone-based RGB-Infrared Cross-Modality Vehicle Detection via Uncertainty-Aware Learning
https://github.com/Sunmi-AI-Lab/head-detection-and-tracking
공식 코드Towards in-store multi-person tracking using head detection and track heatmaps
https://github.com/SunnyCYC/aug4mss
공식 코드Mixing-Specific Data Augmentation Techniques for Improved Blind Violin/Piano Source Separation
https://github.com/SwinTransformer/Feature-Distillation
공식 코드Contrastive Learning Rivals Masked Image Modeling in Fine-tuning via Feature Distillation
https://github.com/SwinTransformer/Transformer-SSL
공식 코드https://github.com/SymbioticLab/FedScale
공식 코드FedScale: Benchmarking Model and System Performance of Federated Learning at Scale
https://github.com/SysCV/qdtrack
공식 코드Quasi-Dense Similarity Learning for Multiple Object Tracking
https://github.com/TAMU-VITA/GAN-Slimming
공식 코드GAN Slimming: All-in-One GAN Compression by A Unified Optimization Framework
https://github.com/TAMU-VITA/ShapeMatchingGAN
공식 코드Controllable Artistic Text Style Transfer via Shape-Matching GAN
https://github.com/TAMU-VITA/UAV-NDFT
공식 코드https://github.com/THU-DA-6D-Pose-Group/Self6D-Diff-Renderer
공식 코드https://github.com/THU-KEG/TWAG
공식 코드https://github.com/THU-MIG/yolov10
공식 코드https://github.com/THUYimingLi/MOVE
공식 코드MOVE: Effective and Harmless Ownership Verification via Embedded External Features
https://github.com/THUYimingLi/backdoor-learning-resources
공식 코드https://github.com/THUrssq/Tianchi04
공식 코드https://github.com/TIBHannover/cross-modal_entity_consistency
공식 코드QuTI! Quantifying Text-Image Consistency in Multimodal Documents
https://github.com/TJUMMG/DS-Net
공식 코드DS-Net: Dynamic Spatiotemporal Network for Video Salient Object Detection
https://github.com/TRAILab/CaDDN
공식 코드Categorical Depth Distribution Network for Monocular 3D Object Detection
https://github.com/TRI-ML/permatrack
공식 코드https://github.com/TRI-ML/sdflabel
공식 코드Autolabeling 3D Objects with Differentiable Rendering of SDF Shape Priors
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.