5 papers
Unlocking the Duality between Flow and Field Matching
Daniil Shlenskii, Alexander Varlamov, Nazar Buzun +1
Conditional Flow Matching (CFM) unifies conventional generative paradigms such as diffusion models and flow matching. Interaction Field Matching (IFM) is a newer framework that gen…
Kandinsky 5.0: A Family of Foundation Models for Image and Video Generation
Vladimir Arkhipkin, Vladimir Korviakov, Nikolai Gerasimenko +22
This report introduces Kandinsky 5.0, a family of state-of-the-art foundation models for high-resolution image and 10-second video synthesis. The framework comprises three core lin…
VARAN: Variational Inference for Self-Supervised Speech Models Fine-Tuning on Downstream Tasks
Daria Diatlova, Nikita Balagansky, Alexander Varlamov +1
Conventional methods for aggregating layers in fine-tuned self-supervised speech models, such as using the final layer or weighted sum, suffer from information bottlenecks and stat…
Novel Loss-Enhanced Universal Adversarial Patches for Sustainable Speaker Privacy
Elvir Karimov, Alexander Varlamov, Danil Ivanov +2
Deep learning voice models are commonly used nowadays, but the safety processing of personal data, such as human identity and speech content, remains suspicious. To prevent malicio…
METR: Image Watermarking with Large Number of Unique Messages
Alexander Varlamov, Daria Diatlova, Egor Spirin
Improvements in diffusion models have boosted the quality of image generation, which has led researchers, companies, and creators to focus on improving watermarking algorithms. Thi…