2 citations · 4 across the 5 of their papers we have counts for
6 papers
Kandinsky 3: Text-to-Image Synthesis for Multifunctional Generative Framework
Vladimir Arkhipkin, Viacheslav Vasilev, Andrei Filatov +9
Text-to-image (T2I) diffusion models are popular for introducing image manipulation methods, such as editing, image fusion, inpainting, etc. At the same time, image-to-video (I2V)…
ESQA: Event Sequences Question Answering
Irina Abdullaeva, Andrei Filatov, Mikhail Orlov +6
Event sequences (ESs) arise in many practical domains including finance, retail, social networks, and healthcare. In the context of machine learning, event sequences can be seen as…
Kandinsky 3.0 Technical Report
Vladimir Arkhipkin, Andrei Filatov, Viacheslav Vasilev +6
We present Kandinsky 3.0, a large-scale text-to-image generation model based on latent diffusion, continuing the series of text-to-image Kandinsky models and reflecting our progres…
Task Discovery: Finding the Tasks that Neural Networks Generalize on
Andrei Atanov, Andrei Filatov, Teresa Yeo +2
When developing deep learning models, we usually decide what task we want to solve then search for a model that generalizes well on the task. An intriguing question would be: what…
Simple Control Baselines for Evaluating Transfer Learning
Andrei Atanov, Shijian Xu, Onur Beker +2
Transfer learning has witnessed remarkable progress in recent years, for example, with the introduction of augmentation-based contrastive self-supervised learning methods. While a…
Fast Line Search for Multi-Task Learning
Andrey Filatov, Daniil Merkulov
Multi-task learning is a powerful method for solving several tasks jointly by learning robust representation. Optimization of the multi-task learning model is a more complex task t…