Efficient Deep Learning in Network Compression and Acceleration

While deep learning delivers state-of-the-art accuracy on many artificial intelligence tasks, it comes at the cost of high computational complexity due to large parameters. It is important to design or develop efficient methods to support deep learning toward enabling its scalable deployment, particularly for embedded devices such as mobile, Internet of things (IOT), and drones. In this chapter, I will present a comprehensive survey of several advanced approaches for efficient deep learning in network compression and acceleration. I will describe the central ideas behind each approach and explore the similarities and differences between different methods. Finally, I will present some future directions in this field

Efficient Privacy Preserving Viola-Jones Type Object Detection via Random Base Image Representation

A cloud server spent a lot of time, energy and money to train a Viola-Jones type object detector with high accuracy. Clients can upload their photos to the cloud server to find objects. However, the client does not want the leakage of the content of his/her photos. In the meanwhile, the cloud server is also reluctant to leak any parameters of the trained object detectors. 10 years ago, Avidan & Butman introduced Blind Vision, which is a method for securely evaluating a Viola-Jones type object detector. Blind Vision uses standard cryptographic tools and is painfully slow to compute, taking a couple of hours to scan a single image. The purpose of this work is to explore an efficient method that can speed up the process. We propose the Random Base Image (RBI) Representation. The original image is divided into random base images. Only the base images are submitted randomly to the cloud server. Thus, the content of the image can not be leaked. In the meanwhile, a random vector and the secure Millionaire protocol are leveraged to protect the parameters of the trained object detector. The RBI makes the integral-image enable again for the great acceleration. The experimental results reveal that our method can retain the detection accuracy of that of the plain vision algorithm and is significantly faster than the traditional blind vision, with only a very low probability of the information leakage theoretically.Comment: 6 pages, 3 figures, To appear in the proceedings of the IEEE International Conference on Multimedia and Expo (ICME), Jul 10, 2017 - Jul 14, 2017, Hong Kong, Hong Kon

Study of Direct Compression Heat Pump Energy-saving Technology

AbstractAnalyzed the feasibility and necessity of the application of heat pump distillation in the gas separation unit. Through the comparison of the results of different heat exchanger, this paper verified the advantages of the heat exchanger with aluminum porous surface tube. Calculated the power consumption of the compressor by Aspen Plus steady-state process simulation, then the value of COP of the heat pump is obtained, and analyzed the economy of the heat pump distillation, the result shows that utilities and operating cost could be decreased by using heat pump distillation in gas separation unit, and the energy utilization efficiency economic benefits and energy-saving effects could be enhanced

Self-Supervised Transformer with Domain Adaptive Reconstruction for General Face Forgery Video Detection

Face forgery videos have caused severe social public concern, and various detectors have been proposed recently. However, most of them are trained in a supervised manner with limited generalization when detecting videos from different forgery methods or real source videos. To tackle this issue, we explore to take full advantage of the difference between real and forgery videos by only exploring the common representation of real face videos. In this paper, a Self-supervised Transformer cooperating with Contrastive and Reconstruction learning (CoReST) is proposed, which is first pre-trained only on real face videos in a self-supervised manner, and then fine-tuned a linear head on specific face forgery video datasets. Two specific auxiliary tasks incorporated contrastive and reconstruction learning are designed to enhance the representation learning. Furthermore, a Domain Adaptive Reconstruction (DAR) module is introduced to bridge the gap between different forgery domains by reconstructing on unlabeled target videos when fine-tuning. Extensive experiments on public datasets demonstrate that our proposed method performs even better than the state-of-the-art supervised competitors with impressive generalization

Predicting Aesthetic Score Distribution through Cumulative Jensen-Shannon Divergence

Aesthetic quality prediction is a challenging task in the computer vision community because of the complex interplay with semantic contents and photographic technologies. Recent studies on the powerful deep learning based aesthetic quality assessment usually use a binary high-low label or a numerical score to represent the aesthetic quality. However the scalar representation cannot describe well the underlying varieties of the human perception of aesthetics. In this work, we propose to predict the aesthetic score distribution (i.e., a score distribution vector of the ordinal basic human ratings) using Deep Convolutional Neural Network (DCNN). Conventional DCNNs which aim to minimize the difference between the predicted scalar numbers or vectors and the ground truth cannot be directly used for the ordinal basic rating distribution. Thus, a novel CNN based on the Cumulative distribution with Jensen-Shannon divergence (CJS-CNN) is presented to predict the aesthetic score distribution of human ratings, with a new reliability-sensitive learning method based on the kurtosis of the score distribution, which eliminates the requirement of the original full data of human ratings (without normalization). Experimental results on large scale aesthetic dataset demonstrate the effectiveness of our introduced CJS-CNN in this task.Comment: AAAI Conference on Artificial Intelligence (AAAI), New Orleans, Louisiana, USA. 2-7 Feb. 201