Examensarbeten och uppsatser / Final Theses

Framläggningar på IDA / Presentations at IDA

Se även framläggningar annonserade hos ISY och ITN i Norrköping / See also presentations announced at ISY and ITN in Norrköping (in Swedish)

If nothing is stated about the presentation language then the presentation is in Swedish.

• 2021-04-23 kl 15:30 i https://liu-se.zoom.us/j/67964170159?pwd=WktBQlJTd3hQdWpRSHlwTzE4NjdTQT09
  
  Group Coordination between Agents using Deep Reinforcement Learning
  
  Författare: Johan Karlsson
  Opponent: Jonathan Lundgren
  Handledare: Johan Källström
  Examinator: Fredrik Heintz
  Nivå: Avancerad (30hp)
  
  Intelligent computer controlled entities can help facilitate research, for example by substituting human controllers. Currently, Reinforcement Learning (RL) show great potential as a tool for researchers to use in complex and difficult problems. However, training time and final behavior of the RL algorithms is heavily dependent on the basic premises of the agents; and on the used algorithms themselves. End-to-end training, that is using raw input data only, is often intractable and simplifications or extensions need to be made to achieve desired behavior without limitless resources. Two different RL algorithms were studied, Deep Deterministic Policy Gradient (DDPG) and Proximal Policy Optimization (PPO), in a 2D multiagent simulator that included non-convex obstacles and limited sensor perception. Desired behavior was movement along a path as a group while avoiding complex obstacles using only a range finder sensor and self-state sensor to perceive their surroundings. Additionally, the usage of extended perception capabilities and demonstrations from an expert model was evaluated in terms of achieved behavior after a set timelimit. It was clear that expert initialization was useful; even using a rudimentary expert lead to better behavior, although considerably slower for PPO. Supplementary input in terms of immediate perception of other agents and the location of the path also helped; for DDPG it was crucial for stable learning.
  
  
• 2021-04-27 kl 12:15 i https://liu-se.zoom.us/j/68814966888?pwd=WXZPc2IzbENOcndpTndreWhvc083Zz09
  
  Evaluating CNN-based Models for Unsupervised Image Denoising
  
  Författare: Johan Lind
  Opponent: Carl Brage
  Handledare: Jonas Wallgren
  Examinator: Cyrille Berger
  Nivå: Avancerad (30hp)
  
  Images are often corrupted by noise which reduces their visual quality and interferes with analysis. Convolutional Neural Networks (CNNs) have become a popular method for denoising images, but their training typically relies on access to thousands of pairs of noisy and clean versions of the same underlying picture. Unsupervised methods lack this requirement and can instead be trained purely using noisy images.
  This thesis evaluated two different unsupervised denoising algorithms: Noise2Self (N2S) and Parametric Probabilistic Noise2Void (PPN2V), both of which train an internal CNN to denoise images.
  Four different CNNs were tested in order to investigate how the performance of these algorithms would be affected by different network architectures.
  The testing used two different datasets: one containing clean images corrupted by synthetic noise, and one containing images damaged by real noise originating from the camera used to capture them.
  Two of the networks, UNet and a CBAM-augmented UNet resulted in high performance competitive with the strong classical denoisers BM3D and NLM.
  The other two networks - GRDN and MultiResUNet - on the other hand caused performance ranging from poor to decent depending on the metric and dataset used.
  
  

Page responsible: Ola Leifler
Last updated: 2020-06-11