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Optimizing Wireless Networks with Deep Unfolding: Comparative Study on Two Deep Unfolding Mechanisms
In the simulation results, we compare the performance of the proposed deep learning models and the iterative solutions in terms of accuracy and inference speed to show their suitability for the real-time application in next generation networks.
P2AT: Pyramid Pooling Axial Transformer for Real-time Semantic Segmentation
In particular, our P2AT variants achieve state-of-art results on the Camvid dataset 80. 5%, 81. 0%, 81. 1% for P2AT-S, P2ATM, and P2AT-L, respectively.
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