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Convolution

CSE 152: Computer Vision Manmohan Chandraker
1 Convolution
Deep Clustering with Convolutional Autoencoders
Tensorizing Neural Networks
Fully Convolutional Mesh Autoencoder Using Efficient Spatially Varying Kernels
Geometrical Aspects of Statistical Learning Theory
Pre-Training Cnns Using Convolutional Autoencoders
Universal Invariant and Equivariant Graph Neural Networks
Understanding 1D Convolutional Neural Networks Using Multiclass Time-Varying Signals Ravisutha Sakrepatna Srinivasamurthy Clemson University, [email protected]
Convolution Network with Custom Loss Function for the Denoising of Low SNR Raman Spectra †
Fighting Deepfake by Exposing the Convolutional Traces on Images
Arxiv:2105.03322V1 [Cs.CL] 7 May 2021
Analysis of Deep Learning Models Using Convolution Neural Network Techniques N.Durai Murugan, SP.Chokkalingam, Samir Brahim Belhaouari 
Deep Learning for Pedestrians: Backpropagation in Cnns
Deepfake Detection by Analyzing Convolutional Traces
Computer Vision I CSE252A Lecture 9
Deep Learning Geodemographics with Autoencoders and Geographic Convolution
Deepfake Image Detection

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1 Convolution Neural Network(CNN) 1.1 Introduction Convolutional Neural Networks(CNN) Are Bio-Inspired Artiﬁcial Neural Networks
Siamese Network-Based Multi-Modal Deepfake Detection
On Complex Valued Convolutional Neural Networks
Application of Convolution Neural Networks and Hydrological Images for the Estimation of Pollutant Loads in Ungauged Watersheds
Convolutional Networks II
5 Convolution of Two Functions
Unleash GPT-2 Power for Event Detection
Continuous Convolutional Neural Networks for Image Classification
Compressing Deep Neural Networks Via Layer Fusion
A Better Autoencoder for Image: Convolutional Autoencoder
Convolutional Networks Overview
Generative Pretraining from Pixels
Novel Convolution Kernels for Computer Vision and Shape Analysis Based on Electromagnetism
Geometry and Tensor Calculus
Understanding Deep Learning Requires Re- Thinking Generalization
And Translation-Equivariant Neural Networks for 3D Point Clouds
Research on Image Classification Model Based on Deep Convolution Neural Network Mingyuan Xin1 and Yong Wang2*
Review of Deep Convolution Neural Network in Image Classification

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