1,681 research outputs found
Robust Conditional GAN from Uncertainty-Aware Pairwise Comparisons
Conditional generative adversarial networks have shown exceptional generation
performance over the past few years. However, they require large numbers of
annotations. To address this problem, we propose a novel generative adversarial
network utilizing weak supervision in the form of pairwise comparisons (PC-GAN)
for image attribute editing. In the light of Bayesian uncertainty estimation
and noise-tolerant adversarial training, PC-GAN can estimate attribute rating
efficiently and demonstrate robust performance in noise resistance. Through
extensive experiments, we show both qualitatively and quantitatively that
PC-GAN performs comparably with fully-supervised methods and outperforms
unsupervised baselines.Comment: Accepted for spotlight at AAAI-2
Deep Learning based Recommender System: A Survey and New Perspectives
With the ever-growing volume of online information, recommender systems have
been an effective strategy to overcome such information overload. The utility
of recommender systems cannot be overstated, given its widespread adoption in
many web applications, along with its potential impact to ameliorate many
problems related to over-choice. In recent years, deep learning has garnered
considerable interest in many research fields such as computer vision and
natural language processing, owing not only to stellar performance but also the
attractive property of learning feature representations from scratch. The
influence of deep learning is also pervasive, recently demonstrating its
effectiveness when applied to information retrieval and recommender systems
research. Evidently, the field of deep learning in recommender system is
flourishing. This article aims to provide a comprehensive review of recent
research efforts on deep learning based recommender systems. More concretely,
we provide and devise a taxonomy of deep learning based recommendation models,
along with providing a comprehensive summary of the state-of-the-art. Finally,
we expand on current trends and provide new perspectives pertaining to this new
exciting development of the field.Comment: The paper has been accepted by ACM Computing Surveys.
https://doi.acm.org/10.1145/328502
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