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ResNet or DenseNet? Introducing Dense Shortcuts to ResNet

ResNet or DenseNet? Nowadays, most deep learning based approaches are
implemented with seminal backbone networks, among them the two arguably most
famous ones are ResNet and DenseNet. Despite their co

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Stable ResNet

Deep ResNet architectures have achieved state of the art performance on many
tasks. While they solve the problem of gradient vanishing, they might suffer
from gradient exploding as the depth becomes l

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A Deep Learning Based Model for Face Mask Recognition

Masked Face Recognition using ResNet-50

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Wider or Deeper: Revisiting the ResNet Model for Visual Recognition

The trend towards increasingly deep neural networks has been driven by a
general observation that increasing depth increases the performance of a
network. Recently, however, evidence has been amassing

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RRL: Representation for Reinforcement Learning

RRL: Resnet as representation for Reinforcement Learning

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RegNet: Self-Regulated Network for Image Classification

The ResNet and its variants have achieved remarkable successes in various
computer vision tasks. Despite its success in making gradient flow through
building blocks, the simple shortcut connection mec

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Pareto-Optimal Quantized ResNet Is Mostly 4-bit

Quantization has become a popular technique to compress neural networks and
reduce compute cost, but most prior work focuses on studying quantization
without changing the network size. Many real-world

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Multiple EffNet/ResNet Architectures for Melanoma Classification

Melanoma is the most malignant skin tumor and usually cancerates from normal
moles, which is difficult to distinguish benign from malignant in the early
stage. Therefore, many machine learning methods

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Regularization in ResNet with Stochastic Depth

Regularization plays a major role in modern deep learning. From classic
techniques such as L1,L2 penalties to other noise-based methods such as
Dropout, regularization often yields better generalizati

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ResNet-LDDMM: Large Deformable Metric Mapping with Deep Residual Neural Networks

ResNet-LDDMM: Advancing the LDDMM Framework Using Deep Residual Networks

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

We discuss relations between Residual Networks (ResNet), Recurrent Neural Networks (RNNs) and the primate visual cortex. We begin with the observation that a shallow RNN is exactly equivalent to a ver

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Residual Learning in Deep Spiking Neural Networks

Deep Residual Learning in Spiking Neural Networks

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HS-ResNet: Hierarchical-Split Block on Convolutional Neural Network

This paper addresses representational block named Hierarchical-Split Block,
which can be taken as a plug-and-play block to upgrade existing convolutional
neural networks, improves model performance si

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ResNeSt: Split-Attention Networks

While image classification models have recently continued to advance, most
downstream applications such as object detection and semantic segmentation
still employ ResNet variants as the backbone netwo

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ResNet strikes back: An improved training procedure in timm

The influential Residual Networks designed by He et al. remain the
gold-standard architecture in numerous scientific publications. They typically
serve as the default architecture in studies, or as ba

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On the Mathematical Understanding of ResNet with Feynman Path Integral

In this paper, we aim to understand Residual Network (ResNet) in a
scientifically sound way by providing a bridge between ResNet and Feynman path
integral. In particular, we prove that the effect of r

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Beyond Finite Layer Neural Networks: Bridging Deep Architectures and Numerical Differential Equations

In our work, we bridge deep neural network design with numerical differential
equations. We show that many effective networks, such as ResNet, PolyNet,
FractalNet and RevNet, can be interpreted as dif

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Densely Connected Residual Network for Attack Recognition

High false alarm rate and low detection rate are the major sticking points for unknown threat perception. To address the problems, in the paper, we present a densely connected residual network (Densel

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RW-Resnet: A Novel Speech Anti-Spoofing Model Using Raw Waveform

In recent years, synthetic speech generated by advanced text-to-speech (TTS)
and voice conversion (VC) systems has caused great harms to automatic speaker
verification (ASV) systems, urging us to desi

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ShakeDrop Regularization for Deep Residual Learning

Overfitting is a crucial problem in deep neural networks, even in the latest
network architectures. In this paper, to relieve the overfitting effect of
ResNet and its improvements (i.e., Wide ResNet,

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