14,899 research outputs found
HoME: a Household Multimodal Environment
We introduce HoME: a Household Multimodal Environment for artificial agents
to learn from vision, audio, semantics, physics, and interaction with objects
and other agents, all within a realistic context. HoME integrates over 45,000
diverse 3D house layouts based on the SUNCG dataset, a scale which may
facilitate learning, generalization, and transfer. HoME is an open-source,
OpenAI Gym-compatible platform extensible to tasks in reinforcement learning,
language grounding, sound-based navigation, robotics, multi-agent learning, and
more. We hope HoME better enables artificial agents to learn as humans do: in
an interactive, multimodal, and richly contextualized setting.Comment: Presented at NIPS 2017's Visually-Grounded Interaction and Language
Worksho
Generative Adversarial Text to Image Synthesis
Automatic synthesis of realistic images from text would be interesting and
useful, but current AI systems are still far from this goal. However, in recent
years generic and powerful recurrent neural network architectures have been
developed to learn discriminative text feature representations. Meanwhile, deep
convolutional generative adversarial networks (GANs) have begun to generate
highly compelling images of specific categories, such as faces, album covers,
and room interiors. In this work, we develop a novel deep architecture and GAN
formulation to effectively bridge these advances in text and image model- ing,
translating visual concepts from characters to pixels. We demonstrate the
capability of our model to generate plausible images of birds and flowers from
detailed text descriptions.Comment: ICML 201
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