Classifying images with an interpretable decision-making process is a long-standing problem in computer vision. In recent years, Prototypical Part Networks has gained traction as an approach for self-explainable neural networks, due to their ability to mimic human visual reasoning by providing explanations based on prototypical object parts. However, the quality of the explanations generated by these methods leaves room for improvement, as the prototypes usually focus on repetitive and redundant concepts. Leveraging recent advances in prototype learning, we present a framework for part-based interpretable image classification that learns a set of semantically distinctive object parts for each class, and provides diverse and comprehensive explanations. The core of our method is to learn the partprototypes in a non-parametric fashion, through clustering deep features extracted from foundation vision models that encode robust semantic information. To quantitatively evaluate the quality of explanations provided by ProtoPNets, we introduce Distinctiveness Score and Comprehensiveness Score. Through evaluation on CUB-200-2011, Stanford Cars and Stanford Dogs datasets, we show that our framework compares favourably against existing ProtoPNets while achieving better interpretability. Code is available at: https://github.com/zijizhu/protonon-param.

We present a spatial-spectral decoupling framework (SDF) to improve the performance of hyperspectral image classification, it mainly contains three modules, including data preprocessing, feature representation, and collaborative decision making. Specifically, the data preprocessing module based on band selection (BS) network can effectively emphasize useful spectral bands while suppressing redundant ones. Besides, the feature representation module is based on spatial-spectral decoupling (SD) network to avoid information confusion between the spatial and the spectral domains. In addition, the collaborative decision making mechanism based on joint optimization can maintain the discriminative properties of different branches and enhance mutual facilitation among them. Finally, the experimental results validate the effectiveness and superiority of our SDF.

We present a spatial–spectral decoupling interaction network for multispectral imagery change detection, which can exploit the underlying information of the multispectral imagery adequately through simultaneously considering the discriminative attribute of each pixel and robust spatial structure of the corresponding patch. Specifically, a 1-D convolutional neural network (1D-CNN) is applied to the spectral vector of each pixel to extract its discriminative feature, while a 2D-CNN is applied to the patch centering on the corresponding pixel to explore the spatial structure information. In addition, an interaction mechanism is incorporated into the feature fusion module to enhance the spatial–spectral consistency.

A logically centralized controller of Software Defined Networking (SDN) is responsible for the management of routing planning and monitoring of network status. With this central control and global visibility, in this paper, we propose a congestion- aware fast failure recovery scheme (CAFFE), which can not only recover the affected flows fast from a wide range of failure scenarios but also avoid potential congestion in the post-recovery network. CAFFE achieves fast recovery by detouring the traffic from neighbour switches as soon as they detect failures and avoids potential congestion by protected paths constructed with the combined consideration of the knowledge of network topology, failure states and network load distribution. We formulate and solve the protected path planning problems where all potential network failures should be protected and the maximum link load in the post- recovery network should be minimized. The resulting protected paths ensure high traffic reachability and low link load even when network failure happens. All these paths are set up a priori by CAFFE. This allows switches to handle failures automatically without the need on waiting for the response from the controller. Experimental results show that CAFFE achieves fast recovery with relatively low overheads on either switches (in terms of backup rule number to configure) or controller (in terms of computational time). It also shows that CAFFE is able to better load-balance the post-recovery network comparing to shortest path re-calculation and OpenFlow-based Segment Protection (OSP).

Among the schemes proposed for failure recovery in software-defined networks, installing backup paths in advance is considered to be an effective approach to reduce the recovery latency. However, the pre-installation poses undue storage overheads on flow tables. In this paper, we propose a customized and cost-efficient backup scheme, which achieves fast recovery from any single-link failure. We introduce an improved breadth first search algorithm to construct the customized backup paths of flows to accommodate their diverse routing demands. By analyzing the path characteristics carefully, we observe that non-conflicted backup paths can be aggregated with each other to reduce the number of backup rules. We formulate this backup path aggregation as an optimization problem. The challenge is how to aggregate the backup paths without causing routing ambiguity. To this end, we design a two-stage aggregation (2SA) algorithm. At its core, 2SA leverages conflict matrixes to guarantee the correctness of aggregation. We evaluate our scheme comprehensively with both the real-world topologies and generated topologies. Simulation results show that our scheme can pre-install the customized backup paths with much fewer, typically one order of magnitude, backup rules compared to the traditional flow-based protection without aggregation.