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New framework enhances Deep JSCC for image classification across domains

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a novel domain-adaptive framework for Deep Joint Source-Channel Coding (Deep JSCC) to improve image classification performance under distribution shifts. The proposed Classification-Capacity-Invariance (CCI) function analyzes how channel capacity and cross-domain invariance impact accuracy. Experiments on digit and PACS datasets demonstrated improved generalization over AWGN and Rayleigh fading channels, achieving 98.15% accuracy on SVHN to MNIST transfer at 10 dB CSNR. AI

IMPACT This research could lead to more robust image classification systems in real-world scenarios with varying data distributions.

RANK_REASON The cluster contains a research paper detailing a new method for Deep JSCC. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New framework enhances Deep JSCC for image classification across domains

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The cluster contains a research paper detailing a new method for Deep JSCC. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Yishen Li, Xuechen Chen, Xiaoheng Deng, Hao Zhang ·

    Domain-Adaptive Deep Joint Source-Channel Coding for Image Classification

    arXiv:2607.28907v1 Announce Type: cross Abstract: Deep joint source--channel coding (Deep JSCC) enables visual semantic transmission by mapping inputs directly to channel symbols and task outputs, but its performance can deteriorate under distribution shifts between training and …

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