Multi-task learning (MTL) is an efficient way to improve the performance of related tasks by sharing knowledge. However, most existing MTL networks run on a single end and are not suitable for collaborative intelligence (CI) scenarios. In this work, we propose network with deep joint source-channel coding (JSCC) framework, which allows operating under CI We first feature fusion based (FFMNet) o...

Source coding is the canonical problem of data compression in information theory. In a locally encodable source coding, each compressed bit depends on only few bits input. this paper, we show that recently popular model semisupervised clustering equivalent to locally encodable coding. model, ta...

Recent works have shown that joint source-channel coding (JSCC) schemes using deep neural networks (DNNs), called DeepJSCC, provide promising results in wireless image transmission. However, these methods mostly focus on the distortion of reconstructed signals with respect to input image, rather than their perception by humans. focusing traditional metrics alone does not necessarily result high...

Given a general channel b over which the uniform X source, denoted by U , is directly communicated within distortion D. The source U puts uniform distribution on all sequences with type precisely pX as compared with the i.i.d. X source which puts ‘most of’ its mass on sequences with type ‘close to’ pX . A randomized covering-packing duality is established between source-coding and channel-codin...