scholarly journals Geo-ALM: POI Recommendation by Fusing Geographical Information and Adversarial Learning Mechanism

2019 ◽  
Author(s):  
Wei Liu ◽  
Zhi-Jie Wang ◽  
Bin Yao ◽  
Jian Yin
Keyword(s):  
Geographical Information ◽  
Generative Adversarial Networks ◽  
Adversarial Learning ◽  
Learning Mechanism ◽  
Adversarial Networks ◽  
The Arts ◽  
Poi Recommendation ◽  
Model Training ◽  
Core Idea ◽  
Geographical Feature

Learning user’s preference from check-in data is important for POI recommendation. Yet, a user usually has visited some POIs while most of POIs are unvisited (i.e., negative samples). To leverage these “no-behavior” POIs, a typical approach is pairwise ranking, which constructs ranking pairs for the user and POIs. Although this approach is generally effective, the negative samples in ranking pairs are obtained randomly, which may fail to leverage “critical” negative samples in the model training. On the other hand, previous studies also utilized geographical feature to improve the recommendation quality. Nevertheless, most of previous works did not exploit geographical information comprehensively, which may also affect the performance. To alleviate these issues, we propose a geographical information based adversarial learning model (Geo-ALM), which can be viewed as a fusion of geographic features and generative adversarial networks. Its core idea is to learn the discriminator and generator interactively, by exploiting two granularity of geographic features (i.e., region and POI features). Experimental results show that Geo- ALM can achieve competitive performance, compared to several state-of-the-arts.

scholarly journals Technical Note: Temporal disaggregation of spatial rainfall fields with generative adversarial networks

2021 ◽  
Vol 25 (6) ◽  
pp. 3207-3225
Author(s):  
Sebastian Scher ◽  
Stefanie Peßenteiner
Keyword(s):  
Probability Distribution ◽  
Temporal Resolution ◽  
Radar Data ◽  
Technical Note ◽  
Geographical Information ◽  
Generative Adversarial Networks ◽  
High Temporal Resolution ◽  
Adversarial Networks ◽  
Rainfall Patterns ◽  
Lower Temporal Resolution

Abstract. Creating spatially coherent rainfall patterns with high temporal resolution from data with lower temporal resolution is necessary in many geoscientific applications. From a statistical perspective, this presents a high- dimensional, highly underdetermined problem. Recent advances in machine learning provide methods for learning such probability distributions. We test the usage of generative adversarial networks (GANs) for estimating the full probability distribution of spatial rainfall patterns with high temporal resolution, conditioned on a field of lower temporal resolution. The GAN is trained on rainfall radar data with hourly resolution. Given a new field of daily precipitation sums, it can sample scenarios of spatiotemporal patterns with sub-daily resolution. While the generated patterns do not perfectly reproduce the statistics of observations, they are visually hardly distinguishable from real patterns. Limitations that we found are that providing additional input (such as geographical information) to the GAN surprisingly leads to worse results, showing that it is not trivial to increase the amount of used input information. Additionally, while in principle the GAN should learn the probability distribution in itself, we still needed expert judgment to determine at which point the training should stop, because longer training leads to worse results.

scholarly journals Recurrent Relational Memory Network for Unsupervised Image Captioning

2020 ◽  
Author(s):  
Dan Guo ◽  
Yang Wang ◽  
Peipei Song ◽  
Meng Wang
Keyword(s):  
Generative Adversarial Networks ◽  
Relational Reasoning ◽  
Relational Memory ◽  
Image Captioning ◽  
Adversarial Networks ◽  
The Arts ◽  
Benchmark Datasets ◽  
Unsupervised Training ◽  
Visual Concepts ◽  
Memory Network

Unsupervised image captioning with no annotations is an emerging challenge in computer vision, where the existing arts usually adopt GAN (Generative Adversarial Networks) models. In this paper, we propose a novel memory-based network rather than GAN, named Recurrent Relational Memory Network (R2M). Unlike complicated and sensitive adversarial learning that non-ideally performs for long sentence generation, R2M implements a concepts-to-sentence memory translator through two-stage memory mechanisms: fusion and recurrent memories, correlating the relational reasoning between common visual concepts and the generated words for long periods. R2M encodes visual context through unsupervised training on images, while enabling the memory to learn from irrelevant textual corpus via supervised fashion. Our solution enjoys less learnable parameters and higher computational efficiency than GAN-based methods, which heavily bear parameter sensitivity. We experimentally validate the superiority of R2M than state-of-the-arts on all benchmark datasets.

scholarly journals Generative Adversarial Learning of Protein Tertiary Structures

Molecules ◽  
2021 ◽  
Vol 26 (5) ◽  
pp. 1209
Author(s):  
Taseef Rahman ◽  
Yuanqi Du ◽  
Liang Zhao ◽  
Amarda Shehu
Keyword(s):  
Protein Molecule ◽  
Generative Models ◽  
Generative Adversarial Networks ◽  
Tertiary Structures ◽  
Adversarial Learning ◽  
Good Balance ◽  
Physiological Conditions ◽  
Adversarial Networks ◽  
Protein Molecules

Protein molecules are inherently dynamic and modulate their interactions with different molecular partners by accessing different tertiary structures under physiological conditions. Elucidating such structures remains challenging. Current momentum in deep learning and the powerful performance of generative adversarial networks (GANs) in complex domains, such as computer vision, inspires us to investigate GANs on their ability to generate physically-realistic protein tertiary structures. The analysis presented here shows that several GAN models fail to capture complex, distal structural patterns present in protein tertiary structures. The study additionally reveals that mechanisms touted as effective in stabilizing the training of a GAN model are not all effective, and that performance based on loss alone may be orthogonal to performance based on the quality of generated datasets. A novel contribution in this study is the demonstration that Wasserstein GAN strikes a good balance and manages to capture both local and distal patterns, thus presenting a first step towards more powerful deep generative models for exploring a possibly very diverse set of structures supporting diverse activities of a protein molecule in the cell.

scholarly journals RIS-GAN: Explore Residual and Illumination with Generative Adversarial Networks for Shadow Removal

2020 ◽  
Vol 34 (07) ◽  
pp. 12829-12836 ◽  
Author(s):  
Ling Zhang ◽  
Chengjiang Long ◽  
Xiaolong Zhang ◽  
Chunxia Xiao
Keyword(s):  
Ground Truth ◽  
Superior Performance ◽  
Generative Adversarial Networks ◽  
Shadow Removal ◽  
Illumination Estimation ◽  
Adversarial Networks ◽  
The Arts ◽  
Benchmark Datasets ◽  
Coarse To Fine ◽  
Ground Truth Information

Residual images and illumination estimation have been proved very helpful in image enhancement. In this paper, we propose a general and novel framework RIS-GAN which explores residual and illumination with Generative Adversarial Networks for shadow removal. Combined with the coarse shadow-removal image, the estimated negative residual images and inverse illumination maps can be used to generate indirect shadow-removal images to refine the coarse shadow-removal result to the fine shadow-free image in a coarse-to-fine fashion. Three discriminators are designed to distinguish whether the predicted negative residual images, shadow-removal images, and the inverse illumination maps are real or fake jointly compared with the corresponding ground-truth information. To our best knowledge, we are the first one to explore residual and illumination for shadow removal. We evaluate our proposed method on two benchmark datasets, i.e., SRD and ISTD, and the extensive experiments demonstrate that our proposed method achieves the superior performance to state-of-the-arts, although we have no particular shadow-aware components designed in our generators.

scholarly journals Disguise Adversarial Networks for Click-through Rate Prediction

2017 ◽  
Author(s):  
Yue Deng ◽  
Yilin Shen ◽  
Hongxia Jin
Keyword(s):  
Supervised Learning ◽  
Generative Adversarial Networks ◽  
Adversarial Learning ◽  
Information Theoretic ◽  
Learning Framework ◽  
Adversarial Networks ◽  
Positive Class ◽  
Assignment Strategy ◽  
Label Distribution ◽  
Click Through Rate

We introduced an adversarial learning framework for improving CTR prediction in Ads recommendation. Our approach was motivated by observing the extremely low click-through rate and imbalanced label distribution in the historical Ads impressions. We hence proposed a Disguise-Adversarial-Networks (DAN) to improve the accuracy of supervised learning with limited positive-class information. In the context of CTR prediction, the rationality behind DAN could be intuitively understood as ``non-clicked Ads makeup''. DAN disguises the disliked Ads impressions (non-clicks) to be interesting ones and encourages a discriminator to classify these disguised Ads as positive recommendations. In an adversarial aspect, the discriminator should be sober-minded which is optimized to allocate these disguised Ads to their inherent classes according to an unsupervised information theoretic assignment strategy. We applied DAN to two Ads datasets including both mobile and display Ads for CTR prediction. The results showed that our DAN approach significantly outperformed other supervised learning and generative adversarial networks (GAN) in CTR prediction.

scholarly journals Technical Note: Temporal Disaggregation of Spatial Rainfall Fields with Generative Adversarial Networks

2020 ◽  
Author(s):  
Sebastian Scher ◽  
Stefanie Peßenteiner
Keyword(s):  
Probability Distribution ◽  
Temporal Resolution ◽  
Radar Data ◽  
Technical Note ◽  
Geographical Information ◽  
Generative Adversarial Networks ◽  
High Temporal Resolution ◽  
Adversarial Networks ◽  
Rainfall Patterns ◽  
Lower Temporal Resolution

Abstract. Creating spatially coherent rainfall patterns with high temporal resolution from data with lower temporal resolution is necessary in many geoscientific applications. From a statistical perspective, this presents a high- dimensional, highly under-determined problem. Recent advances in machine learning provide methods for learning such probability distributions. We test the usage of Generative Adversarial Networks (GANs) for estimating the full probability distribution of spatial rainfall patterns with high temporal resolution, conditioned on a field of lower temporal resolution. The GAN is trained on rainfall radar data with hourly resolution. Given a new field of daily precipitation sums, it can sample scenarios of spatiotemporal patterns with sub-daily resolution. While the generated patterns do not perfectly reproduce the statistics of observations, they are visually hardly distinguishable from real patterns. Limitations that we found are that providing additional input (such as geographical information) to the GAN surprisingly lead to worse results, showing that it is not trivial to increase the amount of used input information. Additionally, while in principle the GAN should learn the probability distribution in itself, we still needed expert judgment to determine at which point the training should stop, because longer training leads to worse results.

Data augment method for machine fault diagnosis using conditional generative adversarial networks

2020 ◽  
Vol 234 (12) ◽  
pp. 2719-2727 ◽  
Author(s):  
Jinrui Wang ◽  
Baokun Han ◽  
Huaiqian Bao ◽  
Mingyan Wang ◽  
Zhenyun Chu ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Data Augmentation ◽  
Fault Classification ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Stacked Autoencoders ◽  
Model Training ◽  
Conditional Information

As a useful data augmentation technique, generative adversarial networks have been successfully applied in fault diagnosis field. But traditional generative adversarial networks can only generate one category fault signals in one time, which is time-consuming and costly. To overcome this weakness, we develop a novel fault diagnosis method which combines conditional generative adversarial networks and stacked autoencoders, and both of them are built by stacking one-dimensional full connection layers. First, conditional generative adversarial networks is used to generate artificial samples based on the frequency samples, and category labels are adopted as the conditional information to simultaneously generate different category signals. Meanwhile, spectrum normalization is added to the discriminator of conditional generative adversarial networks to enhance the model training. Then, the augmented training samples are transferred to stacked autoencoders for feature extraction and fault classification. Finally, two datasets of bearing and gearbox are employed to investigate the effectiveness of the proposed conditional generative adversarial network–stacked autoencoder method.

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