249 citations · 699 across the 39 of their papers we have counts for
6 papers · 1 filter
The Unreasonable Effectiveness of Patches in Deep Convolutional Kernels Methods
Louis Thiry, Michael Arbel, Eugene Belilovsky +1
A recent line of work showed that various forms of convolutional kernel methods can be competitive with standard supervised deep convolutional networks on datasets like CIFAR-10, o…
Compressing the Input for CNNs with the First-Order Scattering Transform
Edouard Oyallon, Eugene Belilovsky, Sergey Zagoruyko +1
We study the first-order scattering transform as a candidate for reducing the signal processed by a convolutional neural network (CNN). We show theoretical and empirical evidence t…
Scaling the Scattering Transform: Deep Hybrid Networks
Edouard Oyallon, Eugene Belilovsky, Sergey Zagoruyko
We use the scattering network as a generic and fixed ini-tialization of the first layers of a supervised hybrid deep network. We show that early layers do not necessarily need to b…
Building a Regular Decision Boundary with Deep Networks
Edouard Oyallon
In this work, we build a generic architecture of Convolutional Neural Networks to discover empirical properties of neural networks. Our first contribution is to introduce a state-o…
Deep Roto-Translation Scattering for Object Classification
Edouard Oyallon, Stéphane Mallat
Dictionary learning algorithms or supervised deep convolution networks have considerably improved the efficiency of predefined feature representations such as SIFT. We introduce a…
Generic Deep Networks with Wavelet Scattering
Edouard Oyallon, Stéphane Mallat, Laurent Sifre
We introduce a two-layer wavelet scattering network, for object classification. This scattering transform computes a spatial wavelet transform on the first layer and a new joint wa…