Demystifying Neural Style Transfer

Neural Style Transfer has recently demonstrated very exciting results which catches eyes in both academia and industry. Despite the amazing results, the principle of neural style transfer, especially why the Gram matrices could represent style remains unclear. In this paper, we propose a novel interpretation of neural style transfer by treating it as a domain adaptation problem. Specifically, we theoretically show that matching the Gram matrices of feature maps is equivalent to minimize the Maximum Mean Discrepancy (MMD) with the second order polynomial kernel. Thus, we argue that the essence of neural style transfer is to match the feature distributions between the style images and the generated images. To further support our standpoint, we experiment with several other distribution alignment methods, and achieve appealing results. We believe this novel interpretation connects these two important research fields, and could enlighten future researches.Comment: Accepted by IJCAI 201

Essays on Food Demand and Food Retail Competition in Local Markets

University of Minnesota Ph.D. dissertation. July 2018. Major: Applied Economics. Advisor: Metin Çakır. 1 computer file (PDF); ix, 153 pages.Consumers become more health-conscious and have higher and more diverse expectations for food quality, ranging from food taste and nutrition features to the characteristics of food production, processing, and marketing. In response, large numbers of new products are introduced and a wide range of changes have occurred in food and agricultural markets. Accordingly, this dissertation comprises three essays investigating questions relevant to these changes and providing implications for food policy and retailing. The first essay focuses on the increasing popularity of the “New Super Grain”—Ethiopian teff. Specifically, we examine Ethiopian consumers’ welfare losses due to increasing teff prices and evaluate the effectiveness of alternative food aid policies in alleviating these losses. Using data from two waves of Ethiopia Socioeconomic Survey 2013-2014 and 2015-2016, we estimate a two-stage demand system and document the consumption patterns of cereals in Ethiopia. We find that teff is the most own-price inelastic grain in the cereal market and a one percent increase in teff prices leads to a decrease of 0.38 percent in total consumer welfare. Subsequently, our results suggest wheat aid is an effective policy in reducing the impacts of increasing teff prices, which lends support to the ongoing Ethiopian policy that distributes subsidized wheat on a large scale. The second essay focuses on the introduction of new demand-enhancing agricultural products. Specifically, we evaluate the welfare impacts of the introduction of Honeycrisp apples. We estimate structural models of consumer demand and retailer competition using store scanner data covering 61 cities across the United States during the period from March 2009 to February 2015. The results show that, on average, the introduction of Honeycrisp apples increases consumer welfare by 3.14 cents per pound, of which 2.98 cents is explained by the increased number of total apple varieties and 0.16 cents by the decline in prices of competing apple varieties. The results also show that the introduction of Honeycrisp apples has increased the total sales quantity by 8.03 percent and the total sales revenue by 21.25 percent over the study period. The third essay examines the food retail competition in local markets by addressing the heterogeneity in households’ choice sets of stores, shopping baskets, and travel distances. A revised mixed logit model is developed to model the household choice of shopping stores that enable us to calculate stores’ price elasticities and recover their gross profit margins under alternative pricing strategies. We construct a dataset for estimation by matching the information in 2016 IRI household and retail scanner datasets. The results show that without considering household travel distance for shopping, we might overestimate stores’ price elasticities and underestimate their gross profit margins. The results also suggest that households prefer to visiting closer stores at expense of paying higher prices for their shopping baskets. Finally, we find that one increase in the number of nearby rivals within 5 kilometers from a store is associated with a decrease of 1.6 to 2.4 percent in the store’s gross profit margin depending on different pricing strategies

Gradient-Guided Dynamic Efficient Adversarial Training

Adversarial training is arguably an effective but time-consuming way to train robust deep neural networks that can withstand strong adversarial attacks. As a response to the inefficiency, we propose the Dynamic Efficient Adversarial Training (DEAT), which gradually increases the adversarial iteration during training. Moreover, we theoretically reveal that the connection of the lower bound of Lipschitz constant of a given network and the magnitude of its partial derivative towards adversarial examples. Supported by this theoretical finding, we utilize the gradient's magnitude to quantify the effectiveness of adversarial training and determine the timing to adjust the training procedure. This magnitude based strategy is computational friendly and easy to implement. It is especially suited for DEAT and can also be transplanted into a wide range of adversarial training methods. Our post-investigation suggests that maintaining the quality of the training adversarial examples at a certain level is essential to achieve efficient adversarial training, which may shed some light on future studies.Comment: 14 pages, 8 figure

Conditional Perceptual Quality Preserving Image Compression

We propose conditional perceptual quality, an extension of the perceptual quality defined in \citet{blau2018perception}, by conditioning it on user defined information. Specifically, we extend the original perceptual quality $d(p_{X},p_{\hat{X}})$ to the conditional perceptual quality $d(p_{X|Y},p_{\hat{X}|Y})$, where $X$ is the original image, $\hat{X}$ is the reconstructed, $Y$ is side information defined by user and $d(.,.)$ is divergence. We show that conditional perceptual quality has similar theoretical properties as rate-distortion-perception trade-off \citep{blau2019rethinking}. Based on these theoretical results, we propose an optimal framework for conditional perceptual quality preserving compression. Experimental results show that our codec successfully maintains high perceptual quality and semantic quality at all bitrate. Besides, by providing a lowerbound of common randomness required, we settle the previous arguments on whether randomness should be incorporated into generator for (conditional) perceptual quality compression. The source code is provided in supplementary material

FCS-HGNN: Flexible Multi-type Community Search in Heterogeneous Information Networks

Community Search (CS), a crucial task in network science, has attracted considerable interest owing to its prowess in unveiling personalized communities, thereby finding applications across diverse domains. Existing research primarily focuses on traditional homogeneous networks, which cannot be directly applied to heterogeneous information networks (HINs). However, existing research also has some limitations. For instance, either they solely focus on single-type or multi-type community search, which severely lacking flexibility, or they require users to specify meta-paths or predefined community structures, which poses significant challenges for users who are unfamiliar with community search and HINs. In this paper, we propose an innovative method, FCS-HGNN, that can flexibly identify either single-type or multi-type communities in HINs based on user preferences. We propose the heterogeneous information transformer to handle node heterogeneity, and the edge-semantic attention mechanism to address edge heterogeneity. This not only considers the varying contributions of edges when identifying different communities, but also expertly circumvents the challenges presented by meta-paths, thereby elegantly unifying the single-type and multi-type community search problems. Moreover, to enhance the applicability on large-scale graphs, we propose the neighbor sampling and depth-based heuristic search strategies, resulting in LS-FCS-HGNN. This algorithm significantly improves training and query efficiency while maintaining outstanding community effectiveness. We conducted extensive experiments on five real-world large-scale HINs, and the results demonstrated that the effectiveness and efficiency of our proposed method, which significantly outperforms state-of-the-art methods.Comment: 13 page

Bit Allocation using Optimization

In this paper, we consider the problem of bit allocation in neural video compression (NVC). Due to the frame reference structure, current NVC methods using the same R-D (Rate-Distortion) trade-off parameter $\lambda$ for all frames are suboptimal, which brings the need for bit allocation. Unlike previous methods based on heuristic and empirical R-D models, we propose to solve this problem by gradient-based optimization. Specifically, we first propose a continuous bit implementation method based on Semi-Amortized Variational Inference (SAVI). Then, we propose a pixel-level implicit bit allocation method using iterative optimization by changing the SAVI target. Moreover, we derive the precise R-D model based on the differentiable trait of NVC. And we show the optimality of our method by proofing its equivalence to the bit allocation with precise R-D model. Experimental results show that our approach significantly improves NVC methods and outperforms existing bit allocation methods. Our approach is plug-and-play for all differentiable NVC methods, and it can be directly adopted on existing pre-trained models