In the past few years, deep convolutional neural networks (CNNs) trained on large image data sets have shown impressive visual object recognition performances. Consequently, these models have attracted the attention of the cognitive science community. Recent studies comparing CNNs with neural data from cortical area IT suggest that CNNs may—in addition to providing good engineering solutions—pr...

In this paper, the dynamical behavior of a class of third-order competitive cellular neural networks (CNNs) depending on two parameters, is studied. The class contains a one-parameter family of symmetric CNNs, which are known to be completely stable. The main result is that it is a generic property within the family of symmetric CNNs that complete stability is robust with respect to (small) non...

This report deals with translation invariance of convolutional neural networks (CNNs) for automatic target recognition (ATR) from synthetic aperture radar (SAR) imagery. In particular, the translation invariance of CNNs for SAR ATR represents the robustness against misalignment of target chips extracted from SAR images. To understand the translation invariance of the CNNs, we trained CNNs which...

Although Convolutional Neural Networks (CNNs) and Long Short Term Memory (LSTM) have yielded impressive performances in a variety of Music Information Retrieval (MIR) tasks, the complementarity among the CNNs of different architectures and that between CNNs and LSTM are seldom considered. In this paper, multichannel CNNs with different architectures and LSTM are combined into one unified archit...

Texture classification is an important and challenging problem in many image processing applications. While convolutional neural networks (CNNs) achieved significant successes for image classification, texture classification remains a difficult problem since textures usually do not contain enough information regarding the shape of object. In image processing, texture classification has been tra...

Graph learning is currently dominated by graph kernels, which, while powerful, suffer some significant limitations. Convolutional Neural Networks (CNNs) offer a very appealing alternative, but processing graphs with CNNs is not trivial. To address this challenge, many sophisticated extensions of CNNs have recently been introduced. In this paper, we reverse the problem: rather than proposing yet...

We introduce Group equivariant Convolutional Neural Networks (G-CNNs), a natural generalization of convolutional neural networks that reduces sample complexity by exploiting symmetries. By convolving over groups larger than the translation group, G-CNNs build representations that are equivariant to these groups, which makes it possible to greatly increase the degree of parameter sharing. We sho...

Deep convolutional neural networks (CNNs) have become one of the most successful methods for image processing tasks in past few years. Recent studies on modern residual architectures, enabling CNNs to be much deeper, have achieved much better results thanks to their high expressive ability by numerous parameters. In general, CNNs are known to have the robustness to the small parallel shift of o...

The purpose of this study is to determine whether current video datasets have sufficient data for training very deep convolutional neural networks (CNNs) with spatio-temporal three-dimensional (3D) kernels. Recently, the performance levels of 3D CNNs in the field of action recognition have improved significantly. However, to date, conventional research has only explored relatively shallow3Darch...

The occurrence of complex dynamic behavior (i.e bifurcation processes, strange and chaotic attractors) in autonomous space-invariant cellular neural networks (CNNs) is investigated. Firstly some sufficient conditions for the instability of CNNs are provided; then some classes of unstable template are identified. Finally it is shown that unstable CNNs often exhibit complex dynamics and for a cas...