Papers with Code 아카이브

Intelligent Communication

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

하위 태스크: 2
평가표: 0
보존된 평가 기록: 0

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/omarmshaal/protobeam

공식 코드

ProtoBeam: Generalizing Deep Beam Prediction to Unseen Antennas using Prototypical Networks

https://github.com/protim1191/glodismo_classifier

공식 코드

Compressed 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 코드

Semantic-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 코드

Semantic-Aware Spectrum Sharing in Internet of Vehicles Based on Deep Reinforcement Learning

https://github.com/recusant7/gan_secom

공식 코드

Evolving Semantic Communication with Generative Model

https://github.com/shubha07m/Energy-optimized-semantic-loss

공식 코드

Transformers for Green Semantic Communication: Less Energy, More Semantics

https://github.com/shubha07m/MetaGreen-Meta-Learning-Inspired-Transformer-Selection

공식 코드

MetaGreen: Meta-Learning Inspired Transformer Selection for Green Semantic Communication

https://github.com/sjtu-mxtao/semantic-communication-systems

공식 코드

Deep Learning-Enabled Semantic Communication Systems with Task-Unaware Transmitter and Dynamic Data

https://github.com/skyd-semantic/drgo-semcom

공식 코드

Distortion Resilience for Goal-Oriented Semantic Communication

https://github.com/tianyuan168326/videosemanticcompression-pytorch

공식 코드

SMC++: Masked Learning of Unsupervised Video Semantic Compression

https://github.com/umut-demirhan/Radar-aided-beam-prediction

공식 코드

Radar Aided 6G Beam Prediction: Deep Learning Algorithms and Real-World Demonstration

https://github.com/wwq5-code/scu

공식 코드

SCU: An Efficient Machine Unlearning Scheme for Deep Learning Enabled Semantic Communications

https://github.com/yl5922/spikacom

공식 코드

SpikACom: A Neuromorphic Computing Framework for Green Communications

https://github.com/zhang-guangyi/semantic-mdjcm

공식 코드

From Analog to Digital: Multi-Order Digital Joint Coding-Modulation for Semantic Communication

https://github.com/zhang-guangyi/t-udeepsc

공식 코드

A Unified Multi-Task Semantic Communication System for Multimodal Data

https://github.com/zhenzi-weng/deepsc-st_demonstration

공식 코드

Deep Learning Enabled Semantic Communications with Speech Recognition and Synthesis

https://github.com/zjs919/irml

공식 코드

Imitation Learning-based Implicit Semantic-aware Communication Networks: Multi-layer Representation and Collaborative Reasoning

https://github.com/ghazigh/MVX

https://github.com/ltdung/snt-sentry

A Semantic-Loss Function Modeling Framework With Task-Oriented Machine Learning Perspectives

https://github.com/meatery/semantic-restoration

Deep Image Semantic Communication Model for Artificial Intelligent Internet of Things

https://github.com/rvlopes/gloria

Scaling Language Models: Methods, Analysis & Insights from Training Gopher

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.