Understanding Deep Convolutional Networks
Deep convolutional networks provide state of the art classifications and regressions results over many high-dimensional problems. We review their architecture, which scatters data with a cascade of linear filter weights and non-linearities. A mathematical framework is introduced to analyze their properties. Computations of invariants involve multiscale contractions, the linearization of hierarchical symmetries, and sparse separations. Applications are discussed.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Understanding 3D CNN Behavior for Alzheimer's Disease Diagnosis from Brain PET Scan
In recent days, Convolutional Neural Networks (CNN) have demonstrated impressive performance in medical image analysis. However, there is a lack of clear understanding of why and how the Convolutional Neural Network perf…
Medical Image AnalysisToward Understanding Convolutional Neural Networks from Volterra Convolution Perspective
We make an attempt to understanding convolutional neural network by exploring the relationship between (deep) convolutional neural networks and Volterra convolutions. We propose a novel approach to explain and study the …
FormConvolutional Patch Networks with Spatial Prior for Road Detection and Urban Scene Understanding
Classifying single image patches is important in many different applications, such as road detection or scene understanding. In this paper, we present convolutional patch networks, which are convolutional networks learne…
Scene UnderstandingImage-based Natural Language Understanding Using 2D Convolutional Neural Networks
We propose a new approach to natural language understanding in which we consider the input text as an image and apply 2D Convolutional Neural Networks to learn the local and global semantics of the sentences from the var…
ClassificationGeneral ClassificationNatural Language UnderstandingOptical Character Recognition+3Towards Understanding Residual and Dilated Dense Neural Networks via Convolutional Sparse Coding
Convolutional neural network (CNN) and its variants have led to many state-of-art results in various fields. However, a clear theoretical understanding about them is still lacking. Recently, multi-layer convolutional spa…