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Semantic Communication
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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/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/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/albert2x/moc_rvq
공식 코드MOC-RVQ: Multilevel Codebook-Assisted Digital Generative Semantic Communication
https://github.com/ant-uni-bremen/sinfony
공식 코드https://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/gmzszu/amp-sc
공식 코드Digital Semantic Communications: An Alternating Multi-Phase Training Strategy with Mask Attack
https://github.com/hqyyqh888/RobustSemanComm
공식 코드Robust Semantic Communications with Masked VQ-VAE Enabled Codebook
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/jaryp/adaptivesemantictokencommunication
공식 코드Adaptive Semantic Token Communication for Transformer-based Edge Inference
https://github.com/jianhuapei/ldm-enabled-semcom-system
공식 코드https://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
https://github.com/protim1191/glodismo_classifier
공식 코드Compressed learning based onboard semantic compression for remote sensing platforms
https://github.com/qiongwu86/semantic-aware-resource-management-for-c-v2x-platooning-via-multi-agent-reinforcement-learning
공식 코드Semantic-Aware Resource Management for C-V2X Platooning via Multi-Agent Reinforcement Learning
https://github.com/qiongwu86/semantic-aware-spectrum-sharing-in-internet-of-vehicles-based-on-deep-reinforcement-learning
공식 코드Semantic-Aware Spectrum Sharing in Internet of Vehicles Based on Deep Reinforcement Learning
https://github.com/recusant7/gan_secom
공식 코드https://github.com/shubha07m/Energy-optimized-semantic-loss
공식 코드Transformers for Green Semantic Communication: Less Energy, More Semantics
https://github.com/shubha07m/MetaGreen-Meta-Learning-Inspired-Transformer-Selection
공식 코드MetaGreen: Meta-Learning Inspired Transformer Selection for Green Semantic Communication
https://github.com/sjtu-mxtao/semantic-communication-systems
공식 코드Deep Learning-Enabled Semantic Communication Systems with Task-Unaware Transmitter and Dynamic Data
https://github.com/skyd-semantic/drgo-semcom
공식 코드Distortion Resilience for Goal-Oriented Semantic Communication
https://github.com/tianyuan168326/videosemanticcompression-pytorch
공식 코드SMC++: Masked Learning of Unsupervised Video Semantic Compression
https://github.com/wwq5-code/scu
공식 코드SCU: An Efficient Machine Unlearning Scheme for Deep Learning Enabled Semantic Communications
https://github.com/yl5922/spikacom
공식 코드SpikACom: A Neuromorphic Computing Framework for Green Communications
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.