| Autor: |
Ivars Namatēvs, Artūrs Ņikuļins, Anda Slaidiņa, Laura Neimane, Oskars Radziņš, Kaspars Sudars |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
|
| Zdroj: |
Information Technology and Management Science, Vol 26, Iss 1, Pp 41-48 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2255-9094 |
| DOI: |
10.7250/itms-2023-0006 |
| Popis: |
Deep neural networks are widely used in computer vision for image classification, segmentation and generation. They are also often criticised as “black boxes” because their decision-making process is often not interpretable by humans. However, learning explainable representations that explicitly disentangle the underlying mechanisms that structure observational data is still a challenge. To further explore the latent space and achieve generic processing, we propose a pipeline for discovering the explainable directions in the latent space of generative models. Since the latent space contains semantically meaningful directions and can be explained, we propose a pipeline to fully resolve the representation of the latent space. It consists of a Dirichlet encoder, conditional deterministic diffusion, a group-swap and a latent traversal module. We believe that this study provides an insight into the advancement of research explaining the disentanglement of neural networks in the community. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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