Network Sketching: Exploiting Binary Structure in Deep CNNs

Convolutional neural networks (CNNs) with deep architectures have substantially advanced the state-of-the-art in computer vision tasks. However, deep networks are typically resource-intensive and thus difficult to be deployed on mobile devices. Recently, CNNs with binary weights have shown compelling efficiency to the community, whereas the accuracy of such models is usually unsatisfactory in practice. In this paper, we introduce network sketching as a novel technique of pursuing binary-weight CNNs, targeting at more faithful inference and better trade-off for practical applications. Our basic idea is to exploit binary structure directly in pre-trained filter banks and produce binary-weight models via tensor expansion. The whole process can be treated as a coarse-to-fine model approximation, akin to the pencil drawing steps of outlining and shading. To further speedup the generated models, namely the sketches, we also propose an associative implementation of binary tensor convolutions. Experimental results demonstrate that a proper sketch of AlexNet (or ResNet) outperforms the existing binary-weight models by large margins on the ImageNet large scale classification task, while the committed memory for network parameters only exceeds a little.Comment: To appear in CVPR201

Reverse knowledge absorptive capacity of MNE-HQ (RKAC): conceptualization, theoretical framework, and empirical testing

Despite the increasing importance of reverse knowledge for innovation and competitive advantage of multinational enterprises (MNEs), the issue of how to make reverse knowledge transfer (RKT) more effective is under-explored. Specifically, what constitutes the absorptive capacity of MNEs' headquarters (HQ), the receiver of reverse knowledge, remains conceptually vague and empirically inconsistent. This study develops a broad conceptualization of MNE-HQs' reverse knowledge absorptive capacity--the RKAC--that integrates two major perspectives, namely motivation-ability view, and process-based view of absorptive capacity. Departing from previous studies that treat absorptive capacity as a generic construct, the broad construct of RKAC is developed for each specific HQ-subsidiary dyad. This study also proposes a theoretical framework that accounts for the antecedents, outcomes, and boundary conditions of RKAC. The proposed model was empirically tested with survey data collected from 206 executive mangers of subsidiaries operating in China. The results supported the theoretical conceptualization and the majority of the proposed hypotheses.Includes bibliographical references

A New Heterogeneous Hybrid MIMO Receive Structure of Rapidly Eliminating DOA Ambiguity

Massive multiple input multiple output(MIMO)-based fully-digital receive antenna arrays eventuate a huge amount of circuit costs and complexity to direction of arrival(DOA) estimation, which is hard to satisfy the needs of high precision and low cost in future green wireless communication. To address this challenge, a novel heterogeneous hybrid MIMO receiver is proposed in this paper and a high performance DOA estimator called heterogeneous cross-minimum distance (HCMD) is developed based on the structure. The antenna arrays are first divided into multiple groups, and each group adopts a different hybrid structure. The virtual antenna arrays of these groups are then used for DOA estimation to generate multiple candidate angle sets, where each set contains a unique true solution and multiple pseudo-solutions. Finally, the cross-distance minimization method is applied to the multiple candidate angle sets to select the corresponding true solution for each group, and the final DOA estimation is given by combining the multiple true solutions. Simulation results show that as the number of antennas tends to large-scale, the proposed method can rapidly find the true solution for each group and achieve excellent estimation performance

Physics Inspired Optimization on Semantic Transfer Features: An Alternative Method for Room Layout Estimation

In this paper, we propose an alternative method to estimate room layouts of cluttered indoor scenes. This method enjoys the benefits of two novel techniques. The first one is semantic transfer (ST), which is: (1) a formulation to integrate the relationship between scene clutter and room layout into convolutional neural networks; (2) an architecture that can be end-to-end trained; (3) a practical strategy to initialize weights for very deep networks under unbalanced training data distribution. ST allows us to extract highly robust features under various circumstances, and in order to address the computation redundance hidden in these features we develop a principled and efficient inference scheme named physics inspired optimization (PIO). PIO's basic idea is to formulate some phenomena observed in ST features into mechanics concepts. Evaluations on public datasets LSUN and Hedau show that the proposed method is more accurate than state-of-the-art methods.Comment: To appear in CVPR 2017. Project Page: https://sites.google.com/view/st-pio