187 citations · 575 across the 29 of their papers we have counts for
6 papers · 1 filter
Label-Efficient Semantic Segmentation with Diffusion Models
Dmitry Baranchuk, Ivan Rubachev, Andrey Voynov +2
Denoising diffusion probabilistic models have recently received much research attention since they outperform alternative approaches, such as GANs, and currently provide state-of-t…
Latent Transformations via NeuralODEs for GAN-based Image Editing
Valentin Khrulkov, Leyla Mirvakhabova, Ivan Oseledets +1
Recent advances in high-fidelity semantic image editing heavily rely on the presumably disentangled latent spaces of the state-of-the-art generative models, such as StyleGAN. Speci…
Distilling the Knowledge from Conditional Normalizing Flows
Dmitry Baranchuk, Vladimir Aliev, Artem Babenko
Normalizing flows are a powerful class of generative models demonstrating strong performance in several speech and vision problems. In contrast to other generative models, normaliz…
Revisiting Deep Learning Models for Tabular Data
Yury Gorishniy, Ivan Rubachev, Valentin Khrulkov +1
The existing literature on deep learning for tabular data proposes a wide range of novel architectures and reports competitive results on various datasets. However, the proposed mo…
Disentangled Representations from Non-Disentangled Models
Valentin Khrulkov, Leyla Mirvakhabova, Ivan Oseledets +1
Constructing disentangled representations is known to be a difficult task, especially in the unsupervised scenario. The dominating paradigm of unsupervised disentanglement is curre…
Functional Space Analysis of Local GAN Convergence
Valentin Khrulkov, Artem Babenko, Ivan Oseledets
Recent work demonstrated the benefits of studying continuous-time dynamics governing the GAN training. However, this dynamics is analyzed in the model parameter space, which result…