Deep Network Classification by Scattering and Homotopy Dictionary Learning

We introduce a sparse scattering deep convolutional neural network, which provides a simple model to analyze properties of deep representation learning for classification. Learning a single dictionary matrix with a classifier yields a higher classification accuracy than AlexNet over the ImageNet 2012 dataset. The network first applies a scattering transform that linearizes variabilities due to geometric transformations such as translations and small deformations. A sparse $\ell^1$ dictionary coding reduces intra-class variability while preserving class separation through projections over unions of linear spaces. It is implemented in a deep convolutional network with a homotopy algorithm having an exponential convergence. A convergence proof is given in a general framework that includes ALISTA. Classification results are analyzed on ImageNet

Use and selection of sleeping sites by proboscis monkeys, Nasalislarvatus, along the Kinabatangan River, Sabah, Malaysia

The choice of a sleeping site is crucial for primates and may influence their survival. In this study, we investigated several tree characteristics influencing the sleeping site selection by proboscis monkeys (Nasalis larvatus) along Kinabatangan River, in Sabah, Malaysia. We identified 81 sleeping trees used by one-male and all-male social groups from November 2011 to January 2012. We recorded 15 variables for each tree. Within sleeping sites, sleeping trees were taller, had a larger trunk, with larger and higher first branches than surrounding trees. The crown contained more mature leaves, ripe and unripe fruits but had vines less often than surrounding trees. In addition, in this study, we also focused on a larger scale, considering sleeping and non-sleeping sites. Multivariate analyses highlighted a combination of 6 variables that revealed the significance of sleeping trees as well as surrounding trees in the selection process. During our boat surveys, we observed that adult females and young individuals stayed higher in the canopy than adult males. This pattern may be driven by their increased vulnerability to predation. Finally, we suggest that the selection of particular sleeping tree features (i.e. tall, high first branch) by proboscis monkeys is mostly influenced by antipredation strategies

The Unreasonable Effectiveness of Patches in Deep Convolutional Kernels Methods

International audienceA 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, obtaining accuracies in the range of 87-90% while being more amenable to theoretical analysis. In this work, we highlight the importance of a data-dependent feature extraction step that is key to obtain good performance in convolutional kernel methods. This step typically corresponds to a whitened dictionary of patches, and gives rise to a data-driven convolutional kernel methods. We extensively study its effect, demonstrating it is the key ingredient for high performance of these methods. Specifically, we show that one of the simplest instances of such kernel methods, based on a single layer of image patches followed by a linear classifier is already obtaining classification accuracies on CIFAR-10 in the same range as previous more sophisticated convolutional kernel methods. We scale this method to the challenging ImageNet dataset, showing such a simple approach can exceed all existing non-learned representation methods. This is a new baseline for object recognition without representation learning methods, that initiates the investigation of convolutional kernel models on ImageNet. We conduct experiments to analyze the dictionary that we used, our ablations showing they exhibit low-dimensional properties

Deep Network Classification by Scattering and Homotopy Dictionary Learning

International audienceWe introduce a sparse scattering deep convolutional neural network, which provides a simple model to analyze properties of deep representation learning for classification. Learning a single dictionary matrix with a classifier yields a higher classification accuracy than AlexNet over the ImageNet 2012 dataset. The network first applies a scattering transform that linearizes variabilities due to geometric transformations such as translations and small deformations. A sparse l1 dictionary coding reduces intra-class variability while preserving class separation through projections over unions of linear spaces. It is implemented in a deep convolutional network with a homotopy algorithm having an exponential convergence. A convergence proof is given in a general framework that includes ALISTA. Classification results are analyzed on ImageNet

Kymatio: Scattering Transforms in Python

The wavelet scattering transform is an invariant signal representation suitable for many signal processing and machine learning applications. We present the Kymatio software package, an easy-to-use, high-performance Python implementation of the scattering transform in 1D, 2D, and 3D that is compatible with modern deep learning frameworks. All transforms may be executed on a GPU (in addition to CPU), offering a considerable speed up over CPU implementations. The package also has a small memory footprint, resulting inefficient memory usage. The source code, documentation, and examples are available undera BSD license at https://www.kymat.io