Publication

How deep convolutional neural networks lose spatial information with training

About
Privacy
Disclaimer

Graph Chatbot

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

Publications related to How deep convolutional neural networks lose spatial information with training | EPFL Graph Search

How deep convolutional neural networks lose spatial information with training

Graph Chatbot

Chat with Graph Search

Explicit representations for Banach subspaces of Lizorkin distributions

Universal Approximation Property of Hamiltonian Deep Neural Networks

Improving the Training of Compact Neural Networks for Visual Recognition

Extrapolation and Spectral Bias of Neural Nets with Hadamard Product: a Polynomial Net Study

Sharp asymptotics on the compression of two-layer neural networks

Impulsive noise removal via a blind CNN enhanced by an iterative post-processing

Approximating Relu Networks By Single-Spike Computation

Neural tensor contractions and the expressive power of deep neural quantum states

A Machine Learning-based Framework for Forecasting Sales of New Products With Short Life Cycles Using Deep Neural Networks

GALET: A deep learning image segmentation model for drone-based grain size analysis of gravel bars