Learning Low-Rank Representations for Model Compression

Vector Quantization (VQ) is an appealing model compression method to obtain a tiny model with less accuracy loss. While methods to obtain better codebooks and codes under fixed clustering dimensionality have been extensively studied, optimizations of the vectors in favour of clustering performance are not carefully considered, especially via the reduction of vector dimensionality. This paper reports our recent progress on the combination of dimensionality compression and vector quantization, proposing a Low-Rank Representation Vector Quantization ($\text{LR}^2\text{VQ}$) method that outperforms previous VQ algorithms in various tasks and architectures. $\text{LR}^2\text{VQ}$ joins low-rank representation with subvector clustering to construct a new kind of building block that is directly optimized through end-to-end training over the task loss. Our proposed design pattern introduces three hyper-parameters, the number of clusters $k$, the size of subvectors $m$ and the clustering dimensionality $\tilde{d}$. In our method, the compression ratio could be directly controlled by $m$, and the final accuracy is solely determined by $\tilde{d}$. We recognize $\tilde{d}$ as a trade-off between low-rank approximation error and clustering error and carry out both theoretical analysis and experimental observations that empower the estimation of the proper $\tilde{d}$ before fine-tunning. With a proper $\tilde{d}$, we evaluate $\text{LR}^2\text{VQ}$ with ResNet-18/ResNet-50 on ImageNet classification datasets, achieving 2.8\%/1.0\% top-1 accuracy improvements over the current state-of-the-art VQ-based compression algorithms with 43$\times$/31$\times$ compression factor

Exploring Key Factors for Contractors in Opening Prefabrication Factories: A Chinese Case Study

Adoption of prefabrication is essential for improving the urban built environment. However, the existing prefabrication market in China is far from mature. As the stakeholder who conducts construction activities, the contractor is facing a dilemma of lacking steady prefabricated components supply. In this circumstance, a potential solution is that contractors open their own prefabrication factories to guarantee stable component supply. The aim of this research is exploring the key factors for contractors to open prefabrication factories. Firstly, a total of 28 influencing factors were identified from literature. Then, the identified factors were divided into four categories: policy environment, market environment, technological environment, and enterprise internal environment. Through interviews with experienced professionals, a total of 19 factors were selected for future analysis. Based on the 19 factors, a questionnaire was designed and distributed to the experts to rate the degree of mutual influences. The collected data were analyzed using Ucinet6.0 software, and the adjacency matrix and the visual models were established. Finally, through the analysis of node centrality, betweenness centrality, and closeness centrality, the four key influencing factors were determined including mandatory implementation policy, precast concrete component's price, market demand, and contractor's strategic objectives. The results of this study could assist contractors in making decisions of opening their own prefabrication factories toward more sustainable environment

Frontier Detection and Reachability Analysis for Efficient 2D Graph-SLAM Based Active Exploration

© 2020 IEEE. We propose an integrated approach to active exploration by exploiting the Cartographer method as the base SLAM module for submap creation and performing efficient frontier detection in the geometrically co-aligned submaps induced by graph optimization. We also carry out analysis on the reachability of frontiers and their clusters to ensure that the detected frontier can be reached by robot. Our method is tested on a mobile robot in real indoor scene to demonstrate the effectiveness and efficiency of our approach

LiDAR Iris for Loop-Closure Detection

© 2020 IEEE. In this paper, a global descriptor for a LiDAR point cloud, called LiDAR Iris, is proposed for fast and accurate loop-closure detection. A binary signature image can be obtained for each point cloud after several LoG-Gabor filtering and thresholding operations on the LiDAR-Iris image representation. Given two point clouds, their similarities can be calculated as the Hamming distance of two corresponding binary signature images extracted from the two point clouds, respectively. Our LiDAR-Iris method can achieve a pose-invariant loop-closure detection at a descriptor level with the Fourier transform of the LiDAR-Iris representation if assuming a 3D (x, y, yaw) pose space, although our method can generally be applied to a 6D pose space by re-aligning point clouds with an additional IMU sensor. Experimental results on five road-scene sequences demonstrate its excellent performance in loop-closure detection