The goal of reinforcement learning (RL) is to build an autonomous agent that takes a sequence of actions to maximize a utility function by interacting with an external, unknown environment. It is a very general learni...The goal of reinforcement learning (RL) is to build an autonomous agent that takes a sequence of actions to maximize a utility function by interacting with an external, unknown environment. It is a very general learning paradigm that can model a wide range of problems, such as games, robotics, autonomous driving, humancomputer interactions, recommendation, healthcare, and many others. In recent years, powered by advances in deep learning and computing power, RL has seen great successes, with AlphaGo/AlphaZero as a prominent example. Such impressive outcomes have sparked fast growing interests in using RL to solve real-life problems.展开更多
In many applications of computer graphics,art,and design,it is desirable for a user to provide intuitive non-image input,such as text,sketch,stroke,graph,or layout,and have a computer system automatically generate pho...In many applications of computer graphics,art,and design,it is desirable for a user to provide intuitive non-image input,such as text,sketch,stroke,graph,or layout,and have a computer system automatically generate photo-realistic images according to that input.While classically,works that allow such automatic image content generation have followed a framework of image retrieval and composition,recent advances in deep generative models such as generative adversarial networks(GANs),variational autoencoders(VAEs),and flow-based methods have enabled more powerful and versatile image generation approaches.This paper reviews recent works for image synthesis given intuitive user input,covering advances in input versatility,image generation methodology,benchmark datasets,and evaluation metrics.This motivates new perspectives on input representation and interactivity,cross fertilization between major image generation paradigms,and evaluation and comparison of generation methods.展开更多
文摘The goal of reinforcement learning (RL) is to build an autonomous agent that takes a sequence of actions to maximize a utility function by interacting with an external, unknown environment. It is a very general learning paradigm that can model a wide range of problems, such as games, robotics, autonomous driving, humancomputer interactions, recommendation, healthcare, and many others. In recent years, powered by advances in deep learning and computing power, RL has seen great successes, with AlphaGo/AlphaZero as a prominent example. Such impressive outcomes have sparked fast growing interests in using RL to solve real-life problems.
基金supported by the National Natural Science Foundation of China(Project Nos.61521002 and 61772298)。
文摘In many applications of computer graphics,art,and design,it is desirable for a user to provide intuitive non-image input,such as text,sketch,stroke,graph,or layout,and have a computer system automatically generate photo-realistic images according to that input.While classically,works that allow such automatic image content generation have followed a framework of image retrieval and composition,recent advances in deep generative models such as generative adversarial networks(GANs),variational autoencoders(VAEs),and flow-based methods have enabled more powerful and versatile image generation approaches.This paper reviews recent works for image synthesis given intuitive user input,covering advances in input versatility,image generation methodology,benchmark datasets,and evaluation metrics.This motivates new perspectives on input representation and interactivity,cross fertilization between major image generation paradigms,and evaluation and comparison of generation methods.
