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DynaComm: Accelerating Distributed CNN Training between Edges and Clouds through Dynamic Communication Scheduling
To reduce uploading bandwidth and address privacy concerns, deep learning at the network edge has been an emerging topic.
QGAN: Quantize Generative Adversarial Networks to Extreme low-bits
Despite the success in model reduction of convolutional neural networks (CNNs), neural network quantization methods have not yet been studied on GANs, which are mainly faced with the issues of both the effectiveness of quantization algorithms and the instability of training GAN models.
QGAN: Quantized Generative Adversarial Networks
The intensive computation and memory requirements of generative adversarial neural networks (GANs) hinder its real-world deployment on edge devices such as smartphones.
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