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GAN Computers Generate Arts? A Survey on Visual Arts, Music, and Literary Text Generation using Generative Adversarial Network

Aug 09, 2021
Sakib Shahriar

"Art is the lie that enables us to realize the truth." - Pablo Picasso. For centuries, humans have dedicated themselves to producing arts to convey their imagination. The advancement in technology and deep learning in particular, has caught the attention of many researchers trying to investigate whether art generation is possible by computers and algorithms. Using generative adversarial networks (GANs), applications such as synthesizing photorealistic human faces and creating captions automatically from images were realized. This survey takes a comprehensive look at the recent works using GANs for generating visual arts, music, and literary text. A performance comparison and description of the various GAN architecture are also presented. Finally, some of the key challenges in art generation using GANs are highlighted along with recommendations for future work.

* 12 pages, 12 figures, 3 tables 

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Fairness in Streaming Submodular Maximization: Algorithms and Hardness

Oct 18, 2020
Marwa El Halabi, Slobodan Mitrović, Ashkan Norouzi-Fard, Jakab Tardos, Jakub Tarnawski

Submodular maximization has become established as the method of choice for the task of selecting representative and diverse summaries of data. However, if datapoints have sensitive attributes such as gender or age, such machine learning algorithms, left unchecked, are known to exhibit bias: under- or over-representation of particular groups. This has made the design of fair machine learning algorithms increasingly important. In this work we address the question: Is it possible to create fair summaries for massive datasets? To this end, we develop the first streaming approximation algorithms for submodular maximization under fairness constraints, for both monotone and non-monotone functions. We validate our findings empirically on exemplar-based clustering, movie recommendation, DPP-based summarization, and maximum coverage in social networks, showing that fairness constraints do not significantly impact utility.

* Accepted to NeurIPS 2020 

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A Baseline Analysis for Podcast Abstractive Summarization

Aug 26, 2020
Chujie Zheng, Harry Jiannan Wang, Kunpeng Zhang, Ling Fan

Podcast summary, an important factor affecting end-users' listening decisions, has often been considered a critical feature in podcast recommendation systems, as well as many downstream applications. Existing abstractive summarization approaches are mainly built on fine-tuned models on professionally edited texts such as CNN and DailyMail news. Different from news, podcasts are often longer, more colloquial and conversational, and noisier with contents on commercials and sponsorship, which makes automatic podcast summarization extremely challenging. This paper presents a baseline analysis of podcast summarization using the Spotify Podcast Dataset provided by TREC 2020. It aims to help researchers understand current state-of-the-art pre-trained models and hence build a foundation for creating better models.

* Accepted for PodRecs: The Workshop on Podcast Recommendations (online), 25th September 2020 

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Stutter Diagnosis and Therapy System Based on Deep Learning

Jul 13, 2020
Gresha Bhatia, Binoy Saha, Mansi Khamkar, Ashish Chandwani, Reshma Khot

Stuttering, also called stammering, is a communication disorder that breaks the continuity of the speech. This program of work is an attempt to develop automatic recognition procedures to assess stuttered dysfluencies and use these assessments to filter out speech therapies for an individual. Stuttering may be in the form of repetitions, prolongations or abnormal stoppages of sounds and syllables. Our system aims to help stutterers by diagnosing the severity and type of stutter and also by suggesting appropriate therapies for practice by learning the correlation between stutter descriptors and the effectiveness of speech therapies on them. This paper focuses on the implementation of a stutter diagnosis agent using Gated Recurrent CNN on MFCC audio features and therapy recommendation agent using SVM. It also presents the results obtained and various key findings of the system developed.

* About stutter classification, severity diagnosis and therapy recommendation 

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Making Metadata Fit for Next Generation Language Technology Platforms: The Metadata Schema of the European Language Grid

Mar 30, 2020
Penny Labropoulou, Katerina Gkirtzou, Maria Gavriilidou, Miltos Deligiannis, Dimitrios Galanis, Stelios Piperidis, Georg Rehm, Maria Berger, Valérie Mapelli, Mickaël Rigault, Victoria Arranz, Khalid Choukri, Gerhard Backfried, José Manuel Gómez Pérez, Andres Garcia Silva

The current scientific and technological landscape is characterised by the increasing availability of data resources and processing tools and services. In this setting, metadata have emerged as a key factor facilitating management, sharing and usage of such digital assets. In this paper we present ELG-SHARE, a rich metadata schema catering for the description of Language Resources and Technologies (processing and generation services and tools, models, corpora, term lists, etc.), as well as related entities (e.g., organizations, projects, supporting documents, etc.). The schema powers the European Language Grid platform that aims to be the primary hub and marketplace for industry-relevant Language Technology in Europe. ELG-SHARE has been based on various metadata schemas, vocabularies, and ontologies, as well as related recommendations and guidelines.

* Proceedings of the 12th Language Resources and Evaluation Conference (LREC 2020). To appear 

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Reducing Exploration of Dying Arms in Mortal Bandits

Jul 04, 2019
Stefano Tracà, Cynthia Rudin, Weiyu Yan

Mortal bandits have proven to be extremely useful for providing news article recommendations, running automated online advertising campaigns, and for other applications where the set of available options changes over time. Previous work on this problem showed how to regulate exploration of new arms when they have recently appeared, but they do not adapt when the arms are about to disappear. Since in most applications we can determine either exactly or approximately when arms will disappear, we can leverage this information to improve performance: we should not be exploring arms that are about to disappear. We provide adaptations of algorithms, regret bounds, and experiments for this study, showing a clear benefit from regulating greed (exploration/exploitation) for arms that will soon disappear. We illustrate numerical performance on the Yahoo! Front Page Today Module User Click Log Dataset.

* Conference: UAI 2019 

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Visual speech recognition: aligning terminologies for better understanding

Oct 03, 2017
Helen L Bear, Sarah Taylor

We are at an exciting time for machine lipreading. Traditional research stemmed from the adaptation of audio recognition systems. But now, the computer vision community is also participating. This joining of two previously disparate areas with different perspectives on computer lipreading is creating opportunities for collaborations, but in doing so the literature is experiencing challenges in knowledge sharing due to multiple uses of terms and phrases and the range of methods for scoring results. In particular we highlight three areas with the intention to improve communication between those researching lipreading; the effects of interchanging between speech reading and lipreading; speaker dependence across train, validation, and test splits; and the use of accuracy, correctness, errors, and varying units (phonemes, visemes, words, and sentences) to measure system performance. We make recommendations as to how we can be more consistent.

* Helen L Bear and Sarah Taylor. Visual speech recognition: aligning terminologies for better understanding. British Machine Vision Conference (BMVC) Deep learning for machine lip reading workshop. 2017 

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Localized Iterative Methods for Interpolation in Graph Structured Data

Oct 09, 2013
Sunil K. Narang, Akshay Gadde, Eduard Sanou, Antonio Ortega

In this paper, we present two localized graph filtering based methods for interpolating graph signals defined on the vertices of arbitrary graphs from only a partial set of samples. The first method is an extension of previous work on reconstructing bandlimited graph signals from partially observed samples. The iterative graph filtering approach very closely approximates the solution proposed in the that work, while being computationally more efficient. As an alternative, we propose a regularization based framework in which we define the cost of reconstruction to be a combination of smoothness of the graph signal and the reconstruction error with respect to the known samples, and find solutions that minimize this cost. We provide both a closed form solution and a computationally efficient iterative solution of the optimization problem. The experimental results on the recommendation system datasets demonstrate effectiveness of the proposed methods.

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Online Learning in a Contract Selection Problem

May 15, 2013
Cem Tekin, Mingyan Liu

In an online contract selection problem there is a seller which offers a set of contracts to sequentially arriving buyers whose types are drawn from an unknown distribution. If there exists a profitable contract for the buyer in the offered set, i.e., a contract with payoff higher than the payoff of not accepting any contracts, the buyer chooses the contract that maximizes its payoff. In this paper we consider the online contract selection problem to maximize the sellers profit. Assuming that a structural property called ordered preferences holds for the buyer's payoff function, we propose online learning algorithms that have sub-linear regret with respect to the best set of contracts given the distribution over the buyer's type. This problem has many applications including spectrum contracts, wireless service provider data plans and recommendation systems.

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An Application to Generate Style Guided Compatible Outfit

May 02, 2022
Debopriyo Banerjee, Harsh Maheshwari, Lucky Dhakad1, Arnab Bhattacharya1, Niloy Ganguly, Muthusamy Chelliah, Suyash Agarwal1

Fashion recommendation has witnessed a phenomenal growth of research, particularly in the domains of shop-the-look, contextaware outfit creation, personalizing outfit creation etc. Majority of the work in this area focuses on better understanding of the notion of complimentary relationship between lifestyle items. Quite recently, some works have realised that style plays a vital role in fashion, especially in the understanding of compatibility learning and outfit creation. In this paper, we would like to present the end-to-end design of a methodology in which we aim to generate outfits guided by styles or themes using a novel style encoder network. We present an extensive analysis of different aspects of our method through various experiments. We also provide a demonstration api to showcase the ability of our work in generating outfits based on an anchor item and styles.

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