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Found 5 papers, 3 papers with code
DeLiGAN : Generative Adversarial Networks for Diverse and Limited Data
A class of recent approaches for generating images, called Generative Adversarial Networks (GAN), have been used to generate impressively realistic images of objects, bedrooms, handwritten digits and a variety of other image modalities.
High Fidelity Semantic Shape Completion for Point Clouds using Latent Optimization
Experiments show that our algorithm is capable of successfully reconstructing point clouds with large missing regions with very high fidelity without having to rely on exemplar based database retrieval.
Community Regularization of Visually-Grounded Dialog
The task of conducting visually grounded dialog involves learning goal-oriented cooperative dialog between autonomous agents who exchange information about a scene through several rounds of questions and answers in natural language.
MAME : Model-Agnostic Meta-Exploration
Meta-Reinforcement learning approaches aim to develop learning procedures that can adapt quickly to a distribution of tasks with the help of a few examples.
Joint inference and input optimization in equilibrium networks
Many tasks in deep learning involve optimizing over the \emph{inputs} to a network to minimize or maximize some objective; examples include optimization over latent spaces in a generative model to match a target image, or adversarially perturbing an input to worsen classifier performance.
