This work is devoted to self-supervised representation learning (SSL). We consider both contrastive and non-contrastive methods and present a new loss function for SSL based on feature whitening. Our solution is conceptually simple and competitive with other methods. Self-supervised representations are beneficial for most areas of deep learning, and reinforcement learning is of particular interest because SSL can compensate for the sparsity of the training signal. We present two methods from this area. The first tackles the partial observability providing the agent with a history, represented with temporal alignment, and improves performance in most Atari environments. The second addresses the exploration problem. The method employs a world model of the SSL latent space, and the prediction error of this model indicates novel states required to explore. It shows strong performance on exploration-hard benchmarks, especially on the notorious Montezuma's Revenge. Finally, we consider the metric learning problem, which has much in common with SSL approaches. We present a new method based on hyperbolic embeddings, vision transformers and contrastive loss. We demonstrate the advantage of hyperbolic space over the widely used Euclidean space for metric learning. The method outperforms the current state-of-the-art by a significant margin.

Surface topography systems enable the capture of spinal dynamic movement. A visualization of possible unique movement patterns appears to be difficult due to large intraclass and small inter-class variabilities. Therefore, we investigated a visualization approach using Siamese neural networks (SNN) and checked, if the identification of individuals is possible based on dynamic spinal data. The presented visualization approach seems promising in visualizing subjects in the presence of intraindividual variability between different gait cycles as well as day-to-day variability. Overall, the results indicate a possible existence of a personal spinal ‘fingerprint’. The work forms the basis for an objective comparison of subjects and the transfer of the method to clinical use cases.

Techniques for imposing a structure on the latent space of neural networks have seen much development in recent years. Clustering techniques used for classification have been used to great success, and with this work we hope to bridge the gap between contrastive losses and Generative models. We introduce an embedding loss derived from Triplet loss to show that attributes and information can be clustered in specific dimensions in the latent space of Variational Auto-Encoders. This allows control over the embedded attributes via manipulation of these latent space dimensions. This work also serves to take steps towards the usage of any data augmentation when applying Triplet loss to Variational Auto-Encoders. In this work three different Variational Auto-Encoders are trained on three different datasets to embed information in three different ways using this novel method. Our results show the method working to varying degrees depending on the implementation and the information embedded. Two experiments using image data and one using waveform audio shows that the method is modality invariant. / Tekniker för att införa en struktur i det latenta utrymmet i neurala nätverk har sett mycket utveckling under de senaste åren. Kluster metoder som används för klassificering har använts till stor framgång, och med detta arbete hoppas vi kunna brygga gapet mellan kontrastiva förlustfunktioner och generativa modeller. Vi introducerar en förlustfunktion för inbäddning härledd från triplet loss för att visa att attribut och information kan klustras i specifika dimensioner i det latenta utrymmet hos variationsautokodare. Detta tillåter kontroll över de inbäddade attributen via manipulering av dessa dimensioner i latenta utrymmet. Detta arbete tjänar också till att ta steg mot användningen av olika data augmentationer när triplet loss tillämpas på generativa modeller. Tre olika Variationsautokodare tränas på tre olika dataset för att bädda in information på tre olika sätt med denna nya metod. Våra resultat visar att metoden fungerar i varierande grad beroende på hur den tillämpas och vilken information som inbäddas. Två experiment använder bild-data och ett använder sig av ljud, vilket visar på att metoden är modalitetsinvariant.