DNN-based frame interpolation--that generates the intermediate frames given two
consecutive frames--typically relies on heavy model architectures with a huge number of …

In image super-resolution, both pixel-wise accuracy and perceptual fidelity are desirable.
However, most deep learning methods only achieve high performance in one aspect due to …

Sparsity-inducing regularization problems are ubiquitous in machine learning applications,
ranging from feature selection to model compression. In this paper, we present a novel …

Optimization problems with group sparse regularization are ubiquitous in various popular
downstream applications, such as feature selection and compression for Deep Neural …

DNN-based frame interpolation, which generates intermediate frames from two consecutive
frames, is often dependent on model architectures with a large number of features …

The compression of deep neural networks (DNNs) to reduce inference cost becomes
increasingly important to meet realistic deployment requirements of various applications …

Convolution neural networks (CNNs) have achieved remarkable success, but typically
accompany high computation cost and numerous redundant weight parameters. To reduce …

Optimizing with group sparsity is significant in enhancing model interpretability in machining
learning applications, eg, feature selection, compressed sensing and model compression …

Abstract Systems and methods for generating interpolated images are disclosed. In
examples, image features are extracted from a first image and a second image; such image …

The performance of a neural network (NN) and/or deep neural network (DNN) can limited by
the number of operations being performed as well as management of data among the …