3 accepted papers
In federated learning (FL), the communication constraint between the remote users and the Parameter Server (PS) is a crucial bottleneck. This paper proposes M22, a rate-distortion inspired approach to model update compression for distributed training of deep neural networks (DNNs). In particular, (i…
We study causal, low-latency, sequential video compression when the output is subjected to both a mean squared-error (MSE) distortion loss as well as a perception loss to target realism. Motivated by prior approaches, we consider two different perception loss functions (PLFs). The first, PLF-JD, co…
Recent years have witnessed significant progress in the area of single image dehazing, thanks to the employment of deep neural networks and diverse datasets. Most of the existing methods perform well when the training and testing are conducted on a single dataset. However, they are not able to handl…