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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…
Local convergence of alternating low-rank optimization methods with overrelaxation
Ivan V. Oseledets, Maxim V. Rakhuba, André Uschmajew
The local convergence of alternating optimization methods with overrelaxation for low-rank matrix and tensor problems is established. The analysis is based on the linearization of…
CC-Cert: A Probabilistic Approach to Certify General Robustness of Neural Networks
Mikhail Pautov, Nurislam Tursynbek, Marina Munkhoeva +3
In safety-critical machine learning applications, it is crucial to defend models against adversarial attacks -- small modifications of the input that change the predictions. Beside…
Tensor-Train Density Estimation
Georgii S. Novikov, Maxim E. Panov, Ivan V. Oseledets
Estimation of probability density function from samples is one of the central problems in statistics and machine learning. Modern neural network-based models can learn high dimensi…
Generation of the NIR spectral Band for Satellite Images with Convolutional Neural Networks
Svetlana Illarionova, Dmitrii Shadrin, Alexey Trekin +2
The near-infrared (NIR) spectral range (from 780 to 2500 nm) of the multispectral remote sensing imagery provides vital information for the landcover classification, especially con…
Object-Based Augmentation Improves Quality of Remote Sensing Semantic Segmentation
Svetlana Illarionova, Sergey Nesteruk, Dmitrii Shadrin +3
Today deep convolutional neural networks (CNNs) push the limits for most computer vision problems, define trends, and set state-of-the-art results. In remote sensing tasks such as…