@mastersthesis {Tempe, title = {Learning in Compressed Domains}, volume = {Doctor of Philosophy in Computer Engineering}, year = {2021}, month = {05/2021}, school = {Arizona State University}, address = {Tempe}, abstract = {

A massive volume of data is generated at an unprecedented rate in the information age. The growth of data significantly exceeds the computing and storage capacities of the existing digital infrastructure. In the past decade, many methods are invented for data compression, compressive sensing and reconstruction, and compressed learning (learning directly upon compressed data) to overcome the data-explosion challenge. While prior works are predominantly model-based, focus on small models, and not suitable for task-oriented sensing or hardware acceleration, the number of available models for compression-related tasks has escalated by orders of magnitude in the past decade. Motivated by this significant growth and the success of big data, this thesis proposes to revolutionize both the compressive sensing reconstruction (CSR) and compressed learning (CL) methods from the data-driven perspective.

In this thesis, a series of topics on data-driven CSR are discussed. Individual data-driven models are proposed for the CSR of bio-signals, images, and videos with improved compression ratio and recovery fidelity trade-off. Specifically, a scalable Laplacian pyramid reconstructive adversarial network (LAPRAN) is proposed for single-image CSR. LAPRAN progressively reconstructs images following the concept of the Laplacian pyramid through the concatenation of multiple reconstructive adversarial networks (RANs). For the CSR of videos, CSVideoNet is proposed to improve the spatial-temporal resolution of reconstructed videos.

Apart from CSR, data-driven CL is discussed in the thesis. A CL framework is proposed to extract features directly from compressed data for image classification, objection detection, and semantic/instance segmentation. Besides, the spectral bias of neural networks is analyzed from the frequency perspective, leading to a learning based frequency selection method for identifying the trivial frequency components which can be removed without accuracy loss. Compared with the conventional spaitial downsampling approaches, the proposed frequency-domain learning method can achieve higher accuracy with reduced input data size.

The methodologies proposed in this thesis are not restricted to the above-mentioned applications. The thesis also discusses other potential applications and directions for future research.

}, keywords = {psclab}, author = {Kai Xu} } @article {562, title = {LAPRAN: A SCALABLE LAPLACIAN PYRAMID RECONSTRUCTIVE ADVERSARIAL NETWORK FOR FLEXIBLE COMPRESSIVE SENSING RECONSTRUCTION}, number = {16/745,817}, year = {2020}, month = {07/2020}, keywords = {psclab}, author = {Fengbo Ren and Kai Xu and Zhikang Zhang} } @inproceedings {Seattle, WA, title = {Learning in the Frequency Domain}, year = {2020}, month = {06/2020}, pages = {1740-1749}, address = {Seattle, WA}, abstract = {

Deep neural networks have achieved remarkable success in computer vision tasks. Existing neural networks mainly operate in the spatial domain with fixed input sizes. For practical applications, images are usually large and have to be downsampled to the predetermined input size of neural networks. Even though the downsampling operations reduce computation and the required communication bandwidth, it removes both redundant and salient information obliviously, which results in accuracy degradation. Inspired by digital signal processing theories, we analyze the spectral bias from the frequency perspective and propose a learning-based frequency selection method to identify the trivial frequency components which can be removed with- out accuracy loss. The proposed method of learning in the frequency domain leverages identical structures of the well- known neural networks, such as ResNet-50, MobileNetV2, and Mask R-CNN, while accepting the frequency-domain information as the input. Experiment results show that learning in the frequency domain with static channel selection can achieve higher accuracy than the conventional spatial downsampling approach and meanwhile further reduce the input data size. Specifically for ImageNet classification with the same input size, the proposed method achieves 1.41\% and 0.66\% top-1 accuracy improvements on ResNet-50 and MobileNetV2, respectively. Even with half input size, the proposed method still improves the top-1 accuracy on ResNet-50 by 1\%. In addition, we observe a 0.8\% average precision improvement on Mask R-CNN for instance segmentation on the COCO dataset.

}, keywords = {psclab}, author = {Kai Xu and Minghai Qin and Fei Sun and Yuhao Wang and Yen-Kuang Chen and Fengbo Ren} } @inproceedings {New Orleans, Louisiana, title = {Light-Weight RetinaNet for Object Detection on Edge Devices}, year = {2020}, month = {04/2020}, address = {New Orleans, Louisiana}, abstract = {

This paper aims at reducing computation for Reti\­nanet, an mAP-30-tier network, to facilitate its practical deploy\­ment on edge devices for providing IoT-based object detection services. We first validate RetinaNet has the best FLOP-mAP trade-off among all mAP-30-tier network. Then, we propose a light-weight RetinaNet structure with effective computation\­-accuracy trade-off by only reducing FLOPs in computationally intensive layers. Compared with the most common way of trading off computation with accuracy-input image scaling, the proposed solution shows a consistently better FLOPs-mAP trade-off curve. Light-weight RetinaNet achieves a 0.3\% mAP improvement at 1.Sx FLOPs reduction point over the original RetinaNet, and gains 1.Sx more energy-efficiency on an Intel Arria 10 FPGA accelerator in the context of edge computing. The proposed method potentially can help a wide range of the object detection applications to move closer to a preferred corner for a better runtime and accuracy, while enjoys more energy-efficient inference at the edge.\ 

}, keywords = {psclab}, author = {YIxing Li and Akshay Dua and Fengbo Ren} } @inproceedings {Munich, Germany, title = {LAPRAN: A Scalable Laplacian Pyramid Reconstructive Adversarial Network for Flexible Compressive Sensing Reconstruction}, year = {2018}, month = {09/2018}, pages = {491-507}, address = {Munich, Germany}, abstract = {

This paper addresses the single-image compressive sensing (CS) and reconstruction problem. We propose a scalable Laplacian pyramid reconstructive adversarial network (LAPRAN) that enables high- fidelity, flexible and fast CS images reconstruction. LAPRAN progressively reconstructs an image following the concept of the Laplacian pyramid through multiple stages of reconstructive adversarial networks (RANs). At each pyramid level, CS measurements are fused with a contextual latent vector to generate a high-frequency image residual. Consequently, LAPRAN can produce hierarchies of reconstructed images and each with an incremental resolution and improved quality. The scalable pyramid structure of LAPRAN enables high-fidelity CS reconstruction with a flexible resolution that is adaptive to a wide range of compression ratios (CRs), which is infeasible with existing methods. Experimental results on multiple public datasets show that LAPRAN offers an average 7.47dB and 5.98dB PSNR, and an average 57.93\% and 33.20 \% SSIM improvement compared to model-based and data-driven baselines, respectively.

}, keywords = {psclab}, author = {Kai Xu and Zhikang Zhang and Fengbo Ren} }