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Stefan Winkler

Publications and source records attributed to Stefan Winkler.

At least 37 records · Page 2Linked to original sources
arXiv2021

Efficient Facial Expression Analysis For Dimensional Affect Recognition Using Geometric Features

Despite their continued popularity, categorical approaches to affect recognition have limitations, especially in real-life situations. Dimensional models of affect offer important advantages for the recognition of subtle expressions and more fine-grained analysis. We introduce a simple but effective facial expression analysis (FEA) system for dimensional affect, solely based on geometric features and Partial Least Squares (PLS) regression. The system jointly learns to estimate Arousal and Valence ratings from a set of facial images. The proposed approach is robust, efficient, and exhibits comparable performance to contemporary deep learning models, while requiring a fraction of the computational resources.

cs.CV↗
arXiv2020

Forecasting Precipitable Water Vapor Using LSTMs

Long-Short-Term-Memory (LSTM) networks have been used extensively for time series forecasting in recent years due to their ability of learning patterns over different periods of time. In this paper, this ability is applied to learning the pattern of Global Positioning System (GPS)-based Precipitable Water Vapor (PWV) measurements over a period of 4 hours. The trained model was evaluated on more than 1500 hours of recorded data. It achieves a root mean square error (RMSE) of 0.098 mm for a forecasting interval of 5 minutes in the future, and outperforms the naive approach for a lead-time of up to 40 minutes.

eess.SP↗
arXiv2020

Empirical Analysis of Overfitting and Mode Drop in GAN Training

We examine two key questions in GAN training, namely overfitting and mode drop, from an empirical perspective. We show that when stochasticity is removed from the training procedure, GANs can overfit and exhibit almost no mode drop. Our results shed light on important characteristics of the GAN training procedure. They also provide evidence against prevailing intuitions that GANs do not memorize the training set, and that mode dropping is mainly due to properties of the GAN objective rather than how it is optimized during training.

cs.LG↗
arXiv2019

A Chaotic Approach on Solar Irradiance Forecasting

We analyse the time series of solar irradiance measurements using chaos theory. The False Nearest Neighbour method (FNN), one of the most common methods of chaotic analysis is used for the analysis. One year data from the weather station located at Nanyang Technological University (NTU) Singapore with a temporal resolution of $1$ minute is employed for the study. The data is sampled at $60$ minutes interval and $30$ minutes interval for the analysis using the FNN method. Our experiments revealed that the optimum dimension required for solar irradiance is $4$ for both samplings. This indicates that a minimum of $4$ dimensions is required for embedding the data for the best representation of input. This study on obtaining the embedding dimension of solar irradiance measurement will greatly assist in fixing the number of previous data required for solar irradiance forecasting.

physics.ao-ph↗
arXiv2019

Predicting Solar Irradiance in Singapore

Solar irradiance is the primary input for all solar energy generation systems. The amount of available solar radiation over time under the local weather conditions helps to decide the optimal location, technology and size of a solar energy project. We study the behaviour of incident solar irradiance on the earth's surface using weather sensors. In this paper, we propose a time-series based technique to forecast the solar irradiance values for shorter lead times of upto 15 minutes. Our experiments are conducted in the tropical region viz. Singapore, which receives a large amount of solar irradiance throughout the year. We benchmark our method with two common forecasting techniques, namely persistence model and average model, and we obtain good prediction performance. We report a root mean square of 147 W/m^2 for a lead time of 15 minutes.

physics.ao-ph↗
arXiv2019

Subjective Quality Assessment of Ground-based Camera Images

Image quality assessment is critical to control and maintain the perceived quality of visual content. Both subjective and objective evaluations can be utilised, however, subjective image quality assessment is currently considered the most reliable approach. Databases containing distorted images and mean opinion scores are needed in the field of atmospheric research with a view to improve the current state-of-the-art methodologies. In this paper, we focus on using ground-based sky camera images to understand the atmospheric events. We present a new image quality assessment dataset containing original and distorted nighttime images of sky/cloud from SWINSEG database. Subjective quality assessment was carried out in controlled conditions, as recommended by the ITU. Statistical analyses of the subjective scores showed the impact of noise type and distortion level on the perceived quality.

eess.IV↗
arXiv2019

PersEmoN: A Deep Network for Joint Analysis of Apparent Personality, Emotion and Their Relationship

Apparent personality and emotion analysis are both central to affective computing. Existing works solve them individually. In this paper we investigate if such high-level affect traits and their relationship can be jointly learned from face images in the wild. To this end, we introduce PersEmoN, an end-to-end trainable and deep Siamese-like network. It consists of two convolutional network branches, one for emotion and the other for apparent personality. Both networks share their bottom feature extraction module and are optimized within a multi-task learning framework. Emotion and personality networks are dedicated to their own annotated dataset. Furthermore, an adversarial-like loss function is employed to promote representation coherence among heterogeneous dataset sources. Based on this, we also explore the emotion-to-apparent-personality relationship. Extensive experiments demonstrate the effectiveness of PersEmoN.

cs.CV↗
arXiv2019

Estimating Solar Irradiance Using Sky Imagers

Ground-based whole sky cameras are extensively used for localized monitoring of clouds nowadays. They capture hemispherical images of the sky at regular intervals using a fisheye lens. In this paper, we propose a framework for estimating solar irradiance from pictures taken by those imagers. Unlike pyranometers, such sky images contain information about cloud coverage and can be used to derive cloud movement. An accurate estimation of solar irradiance using solely those images is thus a first step towards short-term forecasting of solar energy generation based on cloud movement. We derive and validate our model using pyranometers co-located with our whole sky imagers. We achieve a better performance in estimating solar irradiance and in particular its short-term variations as compared to other related methods using ground-based observations.

eess.IV↗
arXiv2019

A Data-Driven Approach for Accurate Rainfall Prediction

In recent years, there has been growing interest in using Precipitable Water Vapor (PWV) derived from Global Positioning System (GPS) signal delays to predict rainfall. However, the occurrence of rainfall is dependent on a myriad of atmospheric parameters. This paper proposes a systematic approach to analyze various parameters that affect precipitation in the atmosphere. Different ground-based weather features like Temperature, Relative Humidity, Dew Point, Solar Radiation, PWV along with Seasonal and Diurnal variables are identified, and a detailed feature correlation study is presented. While all features play a significant role in rainfall classification, only a few of them, such as PWV, Solar Radiation, Seasonal and Diurnal features, stand out for rainfall prediction. Based on these findings, an optimum set of features are used in a data-driven machine learning algorithm for rainfall prediction. The experimental evaluation using a four-year (2012-2015) database shows a true detection rate of 80.4%, a false alarm rate of 20.3%, and an overall accuracy of 79.6%. Compared to the existing literature, our method significantly reduces the false alarm rates.

physics.ao-ph↗
arXiv2019

CloudSegNet: A Deep Network for Nychthemeron Cloud Image Segmentation

We analyze clouds in the earth's atmosphere using ground-based sky cameras. An accurate segmentation of clouds in the captured sky/cloud image is difficult, owing to the fuzzy boundaries of clouds. Several techniques have been proposed that use color as the discriminatory feature for cloud detection. In the existing literature, however, analysis of daytime and nighttime images is considered separately, mainly because of differences in image characteristics and applications. In this paper, we propose a light-weight deep-learning architecture called CloudSegNet. It is the first that integrates daytime and nighttime (also known as nychthemeron) image segmentation in a single framework, and achieves state-of-the-art results on public databases.

physics.ao-ph↗
arXiv2019

Recognition of Advertisement Emotions with Application to Computational Advertising

Advertisements (ads) often contain strong affective content to capture viewer attention and convey an effective message to the audience. However, most computational affect recognition (AR) approaches examine ads via the text modality, and only limited work has been devoted to decoding ad emotions from audiovisual or user cues. This work (1) compiles an affective ad dataset capable of evoking coherent emotions across users; (2) explores the efficacy of content-centric convolutional neural network (CNN) features for AR vis-ã-vis handcrafted audio-visual descriptors; (3) examines user-centric ad AR from Electroencephalogram (EEG) responses acquired during ad-viewing, and (4) demonstrates how better affect predictions facilitate effective computational advertising as determined by a study involving 18 users. Experiments reveal that (a) CNN features outperform audiovisual descriptors for content-centric AR; (b) EEG features are able to encode ad-induced emotions better than content-based features; (c) Multi-task learning performs best among a slew of classification algorithms to achieve optimal AR, and (d) Pursuant to (b), EEG features also enable optimized ad insertion onto streamed video, as compared to content-based or manual insertion techniques in terms of ad memorability and overall user experience.

cs.HC↗
arXiv2019

Predicting GPS-based PWV Measurements Using Exponential Smoothing

Global Positioning System (GPS) derived precipitable water vapor (PWV) is extensively being used in atmospheric remote sensing for applications like rainfall prediction. Many applications require PWV values with good resolution and without any missing values. In this paper, we implement an exponential smoothing method to accurately predict the missing PWV values. The method shows good performance in terms of capturing the seasonal variability of PWV values. We report a root mean square error of 0.1~mm for a lead time of 15 minutes, using past data of 30 hours measured at 5-minute intervals.

physics.ao-ph↗
arXiv2019

Multi-label Cloud Segmentation Using a Deep Network

Different empirical models have been developed for cloud detection. There is a growing interest in using the ground-based sky/cloud images for this purpose. Several methods exist that perform binary segmentation of clouds. In this paper, we propose to use a deep learning architecture (U-Net) to perform multi-label sky/cloud image segmentation. The proposed approach outperforms recent literature by a large margin.

cs.CV↗
arXiv2019

The Unusual Effectiveness of Averaging in GAN Training

We examine two different techniques for parameter averaging in GAN training. Moving Average (MA) computes the time-average of parameters, whereas Exponential Moving Average (EMA) computes an exponentially discounted sum. Whilst MA is known to lead to convergence in bilinear settings, we provide the -- to our knowledge -- first theoretical arguments in support of EMA. We show that EMA converges to limit cycles around the equilibrium with vanishing amplitude as the discount parameter approaches one for simple bilinear games and also enhances the stability of general GAN training. We establish experimentally that both techniques are strikingly effective in the non-convex-concave GAN setting as well. Both improve inception and FID scores on different architectures and for different GAN objectives. We provide comprehensive experimental results across a range of datasets -- mixture of Gaussians, CIFAR-10, STL-10, CelebA and ImageNet -- to demonstrate its effectiveness. We achieve state-of-the-art results on CIFAR-10 and produce clean CelebA face images.\footnote{~The code is available at \url{https://github.com/yasinyazici/EMA_GAN}}

stat.ML↗
arXiv2019

Venn GAN: Discovering Commonalities and Particularities of Multiple Distributions

We propose a GAN design which models multiple distributions effectively and discovers their commonalities and particularities. Each data distribution is modeled with a mixture of $K$ generator distributions. As the generators are partially shared between the modeling of different true data distributions, shared ones captures the commonality of the distributions, while non-shared ones capture unique aspects of them. We show the effectiveness of our method on various datasets (MNIST, Fashion MNIST, CIFAR-10, Omniglot, CelebA) with compelling results.

cs.LG↗
arXiv2018

Investigating the generalizability of EEG-based Cognitive Load Estimation Across Visualizations

We examine if EEG-based cognitive load (CL) estimation is generalizable across the character, spatial pattern, bar graph and pie chart-based visualizations for the nback~task. CL is estimated via two recent approaches: (a) Deep convolutional neural network, and (b) Proximal support vector machines. Experiments reveal that CL estimation suffers across visualizations motivating the need for effective machine learning techniques to benchmark visual interface usability for a given analytic task.

cs.HC↗
arXiv2018

EEG-based Evaluation of Cognitive Workload Induced by Acoustic Parameters for Data Sonification

Data Visualization has been receiving growing attention recently, with ubiquitous smart devices designed to render information in a variety of ways. However, while evaluations of visual tools for their interpretability and intuitiveness have been commonplace, not much research has been devoted to other forms of data rendering, eg, sonification. This work is the first to automatically estimate the cognitive load induced by different acoustic parameters considered for sonification in prior studies. We examine cognitive load via (a) perceptual data-sound mapping accuracies of users for the different acoustic parameters, (b) cognitive workload impressions explicitly reported by users, and (c) their implicit EEG responses compiled during the mapping task. Our main findings are that (i) low cognitive load-inducing (ie, more intuitive) acoustic parameters correspond to higher mapping accuracies, (ii) EEG spectral power analysis reveals higher $α$ band power for low cognitive load parameters, implying a congruent relationship between explicit and implicit user responses, and (iii) Cognitive load classification with EEG features achieves a peak F1-score of 0.64, confirming that reliable workload estimation is achievable with user EEG data compiled using wearable sensors.

cs.HC↗