The remarkable performance of deep Convolutional neural networks (CNNs) is generally
attributed to their deeper and wider architectures, which can come with significant …

This paper reviews the AIM 2020 challenge on efficient single image super-resolution with
focus on the proposed solutions and results. The challenge task was to super-resolve an …

Structural pruning enables model acceleration by removing structurally-grouped parameters
from neural networks. However, the parameter-grouping patterns vary widely across …

Diffusion models are powerful, but they require a lot of time and data to train. We propose
Patch Diffusion, a generic patch-wise training framework, to significantly reduce the training …

We present a simple but powerful architecture of convolutional neural network, which has a
VGG-like inference-time body composed of nothing but a stack of 3x3 convolution and …

The growing energy and performance costs of deep learning have driven the community to
reduce the size of neural networks by selectively pruning components. Similarly to their …

Channel (or 3D filter) pruning serves as an effective way to accelerate the inference of
neural networks. There has been a flurry of algorithms that try to solve this practical problem …

Abstract As designing appropriate Convolutional Neural Network (CNN) architecture in the
context of a given application usually involves heavy human works or numerous GPU hours …

Filter pruning has been widely used for neural network compression because of its enabled
practical acceleration. To date, most of the existing filter pruning works explore the …

Network compression has been widely studied since it is able to reduce the memory and
computation cost during inference. However, previous methods seldom deal with …