Year 2020 Bachelor Thesis
User-Guided Line Art Colorization with Generative Adversarial Networks
We proposed and experimented on multiple conditional Generative Adversarial Networks models for colorizing line art based on the given color hints by the user.
Reinforcement Learning for Automated Vehicle Operation in Simulation
We defined an interface (gyms environment) to SUMO so that modeling of automated driving can be done using inter-vehicle communication and reinforcement learning, and verified that the interface works by conducting reinforcement learning experiments.
Applyng WaveNet for the Generation of Rhythm Game Charts
WaveNet has achieved high accuracy in the Text-To-Speech task. We hypothesized and experimented that it may perform well in chart-generation task.
Using game information and learning strategies in reinforcement learning for NetHack
I applied reinforcement learning to NetHack in order to learn strategies. I proposed the model for this purpose and confirmed its effectiveness.
LoL-V2T: A Video-To-Text Dataset for Video Captioning in Esports
We introduced LoL-V2T: a large video-to-text dataset and generated captions for esports footage with deep learning.