Ehdolliset normalisoivat virtaukset kuvien käänteisongelmissa

Learning-based methods have provided powerful tools for solving classification and regression -related problems yielding far superior results to classical handcrafted rule-based models. These models have proven to be efficient in multiple domains in many different fields. However, many common problems are inherently illposed and lack a unique answer hence requiring a regularization pass or alternatively a probabilistic framework for successful modeling. While many different families of models capable of learning distributions given samples exist, they commonly resort to approximations or surrogate training objectives. In this thesis we solve image-related inverse problems with a family of probabilistic models known as conditional normalizing flows. A normalizing flow consists of repeated applications of invertible transformations on a simple prior distribution rendering it into a more complex distribution with direct and tractable probability density evaluation and efficient sampling. We show that a conditional normalizing flow is able to provide plausible, high-quality samples with visible benign variance from a conditional distribution in image super resolution, denoising and colorization tasks. We quantify the success of the model as well as its shortcomings and inspect how it internally addresses the conversion of white noise into a realistic image.Havainnoista oppimiseen optimoinnin avulla perustuvat mallit kykenevät ratkaisemaan monia ongelmia huomattavasti tehokkaammin, kuin klassiset staattisiin päätössääntöihin perustuvat mallit. Perinteisesti mallit antavat yleensä kuitenkin vain yhden vastauksen, vaikka useilla ongelmilla saattaa olla monta keskenään yhtä hyväksyttävää vastausta. Tämän takia on tarkoituksenmukaista mallintaa todennäköisyysjakaumaa kaikista mahdollisista vastauksista yksittäisen vastauksen sijaan. Tässä diplomityössä tutkitaan normalisoivien virtausten malliluokan soveltamista digitaalisiin kuviin liittyviin käänteisongelmiin. Normalisoiva virtaus muuntaa yksinkertaisen todennäköisyysjakauman neuroverkoilla parametrosoiduilla kääntyvillä funktioilla monimutkaisemmaksi jakaumaksi, siten että havaintojen uskottavuudesta saadaan kuitenkin tarkka numeerinen arvo. Normalisoivat virtaukset mahdollistavat myös tehokkaan näytteiden ottamisen niiden mallintamasta monimutkaisesta todennäköisyysjakaumasta. Työssä määritetään, kuinka hyvin virtausmallit onnistuvat tehtävässään ja kuinka ne muodostavat uskottavia kuvia kohinasta. Työssä todetaan, että ehdollisten normalisoivien virtausten avulla voidaan tuottaa korkealaatuisia näytteitä useissa kuviin liittyvissä käänteisongelmissa

Kanerva++: extending The Kanerva Machine with differentiable, locally block allocated latent memory

Episodic and semantic memory are critical components of the human memory model. The theory of complementary learning systems (McClelland et al., 1995) suggests that the compressed representation produced by a serial event (episodic memory) is later restructured to build a more generalized form of reusable knowledge (semantic memory). In this work we develop a new principled Bayesian memory allocation scheme that bridges the gap between episodic and semantic memory via a hierarchical latent variable model. We take inspiration from traditional heap allocation and extend the idea of locally contiguous memory to the Kanerva Machine, enabling a novel differentiable block allocated latent memory. In contrast to the Kanerva Machine, we simplify the process of memory writing by treating it as a fully feed forward deterministic process, relying on the stochasticity of the read key distribution to disperse information within the memory. We demonstrate that this allocation scheme improves performance in memory conditional image generation, resulting in new state-of-the-art conditional likelihood values on binarized MNIST (<=41.58 nats/image) , binarized Omniglot (<=66.24 nats/image), as well as presenting competitive performance on CIFAR10, DMLab Mazes, Celeb-A and ImageNet32x32

Microstructure reconstruction using diffusion-based generative models

Microstructure reconstruction has been an essential part of computational material engineering to reveal the relationship between the microstructures and the material properties. However, it is still challenging to find a general solution for microstructure characterization and reconstruction (MCR) tasks although there have been many attempts such as the descriptor-based reconstruction methods. To address this generality problem, the denoising diffusion probabilistic models are first employed for the microstructure reconstruction task which can be applied to various types of material systems. Several microstructures (e.g., carbonate, ceramics, copolymer, etc.) are considered to be reproduced for validating the proposed models while addressing the quality of the generated images with the quantitative evaluation metrics (FID score, precision and recall). The results show that the proposed diffusion model based approach is applicable for reproducing various types of microstructures with different spatial distributions of morphological features. The present approach also provides a stable training procedure with simple implementation for generating visually similar microstructures (and also statistically equivalent) without requiring expert knowledge and some time-consuming parametric studies. The proposed approach has the potential of being a universal microstructure reconstruction method for handling complex microstructures for materials science