Resnet in Resnet: Generalizing Residual Architectures

03/25/2016
by   Sasha Targ, et al.
0

Residual networks (ResNets) have recently achieved state-of-the-art on challenging computer vision tasks. We introduce Resnet in Resnet (RiR): a deep dual-stream architecture that generalizes ResNets and standard CNNs and is easily implemented with no computational overhead. RiR consistently improves performance over ResNets, outperforms architectures with similar amounts of augmentation on CIFAR-10, and establishes a new state-of-the-art on CIFAR-100.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
05/05/2022

Biologically inspired deep residual networks for computer vision applications

Deep neural network has been ensured as a key technology in the field of...
research
01/03/2021

RegNet: Self-Regulated Network for Image Classification

The ResNet and its variants have achieved remarkable successes in variou...
research
11/11/2018

ReSet: Learning Recurrent Dynamic Routing in ResNet-like Neural Networks

Neural Network is a powerful Machine Learning tool that shows outstandin...
research
01/15/2020

Deep Residual Flow for Novelty Detection

The effective application of neural networks in the real-world relies on...
research
07/06/2017

Dual Path Networks

In this work, we present a simple, highly efficient and modularized Dual...
research
04/13/2016

Bridging the Gaps Between Residual Learning, Recurrent Neural Networks and Visual Cortex

We discuss relations between Residual Networks (ResNet), Recurrent Neura...
research
02/25/2018

Functional Gradient Boosting based on Residual Network Perception

Residual Networks (ResNets) have become state-of-the-art models in deep ...

Please sign up or login with your details

Forgot password? Click here to reset