2022
AAAI
AAAI 2022
Increasing the Diversity of Deep Generative Models
Abstract
Abstract Generative models are used in a variety of applications that require diverse output. Yet, models are primarily optimised for sample fidelity and mode coverage. My work aims to increase the output diversity of generative models for multi-solution tasks. Previously, we analysed the use of generative models in artistic settings and how its objective diverges from distribution fitting. For specific use cases, we quantified the limitations of generative models. Future work will focus on adapting generative modelling for downstream tasks that require a diverse set of high-quality artefacts.
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Interdisciplinary Bridge
— Computer Vision and Deep Learning and Machine Learning
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Keyword Pioneer
— artistic setting
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio
Authors
Topics
Machine Learning > Learning Types > Self-Supervised Learning
Deep Learning > Models > Generative Models
Computer Vision > Generation > Image Generation
Machine Learning > Learning Types > Representation Learning
Machine Learning > Learning Types > Deep Learning
Deep Learning > Optimization & Theory > Optimization
Deep Learning > Learning Types > Generative Models