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Found 8 papers, 4 papers with code
Unsupervised Real Image Super-Resolution via Generative Variational AutoEncoder
Depending upon whether using a discriminator or not, a deep convolutional neural network can provide an image with high fidelity or better perceptual quality.
Multiple Style Transfer via Variational AutoEncoder
Modern works on style transfer focus on transferring style from a single image.
Understanding reinforcement learned crowds
Each of these choices has a significant, and potentially nontrivial impact on the results, and so researchers should be mindful about choosing and reporting them in their work.
See360: Novel Panoramic View Interpolation
We present See360, which is a versatile and efficient framework for 360 panoramic view interpolation using latent space viewpoint estimation.
AIM 2020 Challenge on Real Image Super-Resolution: Methods and Results
This paper introduces the real image Super-Resolution (SR) challenge that was part of the Advances in Image Manipulation (AIM) workshop, held in conjunction with ECCV 2020.
A Survey on Reinforcement Learning Methods in Character Animation
Reinforcement Learning is an area of Machine Learning focused on how agents can be trained to make sequential decisions, and achieve a particular goal within an arbitrary environment.
UGAE: A Novel Approach to Non-exponential Discounting
We also show experimentally that agents with non-exponential discounting trained via UGAE outperform variants trained with Monte Carlo advantage estimation.
Reward Function Design for Crowd Simulation via Reinforcement Learning
Crowd simulation is important for video-games design, since it enables to populate virtual worlds with autonomous avatars that navigate in a human-like manner.
