Add to ORKGIn this work, we suggest Kernel Filtering Linear Overparameterization (KFLO), where a linear cascade of filtering layers is used during training to improve network performance in test time. We implement this cascade in a kernel filtering fashion, which prevents the trained architecture from becoming unnecessarily deeper. This also allows using our approach with almost any network architecture and let combining the filtering layers into a single layer in test time. Thus, our approach does not add computational complexity during inference. We demonstrate the advantage of KFLO on various network models and datasets in supervised learning.Comment: Accepted to ICIP 2022. Code available at https://github.com/AmitHenig/KFL
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International audienceModern neural networks are over-parametrized. In particular, each rectified li...
Modern neural networks often have great expressive power and can be trained to overfit the training ...
Convolutional Neural Networks (CNNs) are brain-inspired computational models designed to recognize p...
This paper introduces the Point Cloud Network (PCN) architecture, a novel implementation of linear l...
When a large feedforward neural network is trained on a small training set, it typically performs po...
Neural networks are more expressive when they have multiple layers. In turn, conventional training m...
Benign overfitting, the phenomenon where interpolating models generalize well in the presence of noi...
Advanced deep learning architectures consist of tens of fully connected and convolutional hidden lay...
A, Schematic of a sparsely connected network with 3 hidden layers. The output layer is fully connect...
Recently, neural tangent kernel (NTK) has been used to explain the dynamics of learning parameters o...
For many reasons, neural networks have become very popular AI machine learning models. Two of the mo...
We propose a novel method for training a neural network for image classification to reduce input dat...
In this paper we introduce a novel neural network architecture, in which weight matrices are re-para...
A feed-forward neural network artificial model, or multilayer perceptron (MLP), learns input samples...
Over the past few years, deep neural networks have been at the center of attention in machine learn...
International audienceModern neural networks are over-parametrized. In particular, each rectified li...
Modern neural networks often have great expressive power and can be trained to overfit the training ...
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