Papers with Code 아카이브
Intelligent Communication
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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Intelligently decide (i) the content of data to be shared/communicated and (ii) the direction in which the chosen data is transmitted.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/HQXie0910/Lite-DeepSC
공식 코드A Lite Distributed Semantic Communication System for Internet of Things
https://github.com/Hxxxz0/RMDM
공식 코드https://github.com/JiangkaiWu/Promptus
공식 코드Promptus: Can Prompts Streaming Replace Video Streaming with Stable Diffusion
https://github.com/Kiven-ykw/TW-SemanticComm
공식 코드https://github.com/Lizonghang/PerSF-SemCom
공식 코드https://github.com/Richardzhangxx/PET-CGDNN
공식 코드https://github.com/YL12345/QoE-Aware-Resource-Allocation-for-Semantic-Communication-Networks
공식 코드QoE-Aware Resource Allocation for Semantic Communication Networks
https://github.com/YL12345/semantic-resource-allocation-S-SE-
공식 코드https://github.com/ZJU-IICNS-AICOMM/Multihop-DeepSC
공식 코드Alleviating Distortion Accumulation in Multi-Hop Semantic Communication
https://github.com/Zhenzi-Weng/DeepSC-S
공식 코드https://github.com/Zhenzi-Weng/LaSC-ST
공식 코드Large Model Empowered Streaming Speech Semantic Communications
https://github.com/acyiobs/digital_twin
공식 코드Digital Twin Based Beam Prediction: Can we Train in the Digital World and Deploy in Reality?
https://github.com/acyiobs/lidar_beam_tracking
공식 코드LiDAR Aided Future Beam Prediction in Real-World Millimeter Wave V2I Communications
https://github.com/albert2x/moc_rvq
공식 코드MOC-RVQ: Multilevel Codebook-Assisted Digital Generative Semantic Communication
https://github.com/ant-uni-bremen/sinfony
공식 코드https://github.com/ardiantovn/gpsbeam
공식 코드GPS-Aided Deep Learning for Beam Prediction and Tracking in UAV mmWave Communication
https://github.com/chunbaobao/genet
공식 코드GeNet: A Graph Neural Network-based Anti-noise Task-Oriented Semantic Communication Paradigm
https://github.com/cqilab-official/gensc-6g
공식 코드GenSC-6G: A Prototype Testbed for Integrated Generative AI, Quantum, and Semantic Communication
https://github.com/gauthamkrishna-g/Dynamic-Gesture-Recognition
공식 코드A Generic Multi-modal Dynamic Gesture Recognition System using Machine Learning
https://github.com/gmzszu/amp-sc
공식 코드Digital Semantic Communications: An Alternating Multi-Phase Training Strategy with Mask Attack
https://github.com/gourangc/Vision-Position-Beam-Prediction
공식 코드Vision-Position Multi-Modal Beam Prediction Using Real Millimeter Wave Datasets
https://github.com/hqyyqh888/RobustSemanComm
공식 코드Robust Semantic Communications with Masked VQ-VAE Enabled Codebook
https://github.com/hyun-ryu/emc2net
공식 코드EMC2-Net: Joint Equalization and Modulation Classification based on Constellation Network
https://github.com/ispamm/gesco
공식 코드Generative Semantic Communication: Diffusion Models Beyond Bit Recovery
https://github.com/ispamm/img2img-sc
공식 코드Language-Oriented Semantic Latent Representation for Image Transmission
https://github.com/ispamm/q-gesco
공식 코드Lightweight Diffusion Models for Resource-Constrained Semantic Communication
https://github.com/itu-ai-ml-in-5g-challenge/deepsense6g_tii
공식 코드Multimodal Transformers for Wireless Communications: A Case Study in Beam Prediction
https://github.com/jaryp/adaptivesemantictokencommunication
공식 코드Adaptive Semantic Token Communication for Transformer-based Edge Inference
https://github.com/jianhuapei/ldm-enabled-semcom-system
공식 코드https://github.com/jmoraispk/position-beam-prediction
공식 코드Position Aided Beam Prediction in the Real World: How Useful GPS Locations Actually Are?
https://github.com/liyang619/MAFENN_TRANS_2022
공식 코드Multi-Agent Feedback Enabled Neural Networks for Intelligent Communications
https://github.com/lukun199/semanticrl
공식 코드Reinforcement Learning-powered Semantic Communication via Semantic Similarity
https://github.com/lynshao/semanticpapr
공식 코드Semantic Communications with Discrete-time Analog Transmission: A PAPR Perspective
https://github.com/meatery/semantic-segmentation
공식 코드Deep Image Semantic Communication Model for Artificial Intelligent Internet of Things
https://github.com/omarerak/clad
공식 코드Contrastive Learning and Adversarial Disentanglement for Task-Oriented Semantic Communications
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.