Advanced Efficient Strategy for Detection of Dark Objects Based on Spiking Network with Multi-Box Detection

Several deep learning algorithms have shown amazing performance for existing object detection tasks, but recognizing darker objects is the largest challenge. Moreover, those techniques struggled to detect or had a slow recognition rate, resulting in significant performance losses. As a result, an improved and accurate detection approach is required to address the above difficulty. The whole study proposes a combination of spiked and normal convolution layers as an energy-efficient and reliable object detector model. The proposed model is split into two sections. The first section is developed as a feature extractor, which utilizes pre-trained VGG16, and the second section of the proposal structure is the combination of spiked and normal Convolutional layers to detect the bounding boxes of images. We drew a pre-trained model for classifying detected objects. With state of the art Python libraries, spike layers can be trained efficiently. The proposed spike convolutional object detector (SCOD) has been evaluated on VOC and Ex-Dark datasets. SCOD reached 66.01% and 41.25% mAP for detecting 20 different objects in the VOC-12 and 12 objects in the Ex-Dark dataset. SCOD uses 14 Giga FLOPS for its forward path calculations. Experimental results indicated superior performance compared to Tiny YOLO, Spike YOLO, YOLO-LITE, Tinier YOLO and Center of loc+Xception based on mAP for the VOC dataset

Weight-dependent Gates for Differentiable Neural Network Pruning

In this paper, we propose a simple and effective network pruning framework, which introduces novel weight-dependent gates to prune filter adaptively. We argue that the pruning decision should depend on the convolutional weights, in other words, it should be a learnable function of filter weights. We thus construct the weight-dependent gates (W-Gates) to learn the information from filter weights and obtain binary filter gates to prune or keep the filters automatically. To prune the network under hardware constraint, we train a Latency Predict Net (LPNet) to estimate the hardware latency of candidate pruned networks. Based on the proposed LPNet, we can optimize W-Gates and the pruning ratio of each layer under latency constraint. The whole framework is differentiable and can be optimized by gradient-based method to achieve a compact network with better trade-off between accuracy and efficiency. We have demonstrated the effectiveness of our method on Resnet34, Resnet50 and MobileNet V2, achieving up to 1.33/1.28/1.1 higher Top-1 accuracy with lower hardware latency on ImageNet. Compared with state-of-the-art pruning methods, our method achieves superior performance.Comment: ECCV worksho

Robust L-Isomap with a Novel Landmark Selection Method

Isomap is a widely used nonlinear method for dimensionality reduction. Landmark-Isomap (L-Isomap) has been proposed to improve the scalability of Isomap. In this paper, we focus on two important issues that were not taken into account in L-Isomap, landmark point selection and topological stability. At first, we present a novel landmark point selection method. It first uses a greedy strategy to select some points as landmark candidates and then removes the candidate points that are neighbours of other candidates. The remaining candidate points are the landmark points. The selection method can promote the computation efficiency without sacrificing accuracy. For the topological stability, we define edge density for each edge in the neighbourhood graph. According to the geometrical characteristic of the short-circuit edges, we provide a method to eliminate the short-circuit edge without breaking the data integrity. The approach that integrates L-Isomap with these two improvements is referred to as Robust L-Isomap (RL-Isomap). The effective performance of RL-Isomap is confirmed through several numerical experiments

Finding optimal memoryless policies of POMDPs under the expected average reward criterion

In this paper, partially observable Markov decision processes (POMDPs) with discrete state and action space under the average reward criterion are considered from a recent-developed sensitivity point of view. By analyzing the average-reward performance difference formula, we propose a policy iteration algorithm with step sizes to obtain an optimal or local optimal memoryless policy. This algorithm improves the policy along the same direction as the policy iteration does and suitable step sizes guarantee the convergence of the algorithm. Moreover, the algorithm can be used in Markov decision processes (MDPs) with correlated actions. Two numerical examples are provided to illustrate the applicability of the algorithm.POMDPs Performance difference Policy iteration with step sizes Correlated actions Memoryless policy

An Incentive Scheme Based on Contribution for Peer-to-Peer File Sharing Systems

Volume 3 Issue 7 (July 2015