no code implementations • ICLR 2021 • Stanislav Morozov, Andrey Voynov, Artem Babenko
The embeddings from CNNs pretrained on Imagenet classification are de-facto standard image representations for assessing GANs via FID, Precision and Recall measures.
1 code implementation • 8 Jun 2020 • Andrey Voynov, Stanislav Morozov, Artem Babenko
The recent rise of unsupervised and self-supervised learning has dramatically reduced the dependency on labeled data, providing effective image representations for transfer to downstream vision tasks.
1 code implementation • NeurIPS 2019 • Denis Mazur, Vage Egiazarian, Stanislav Morozov, Artem Babenko
Our main contribution is PRODIGE: a method that learns a weighted graph representation of data end-to-end by gradient descent.
5 code implementations • ICLR 2020 • Sergei Popov, Stanislav Morozov, Artem Babenko
In this paper, we introduce Neural Oblivious Decision Ensembles (NODE), a new deep learning architecture, designed to work with any tabular data.
1 code implementation • 19 Aug 2019 • Stanislav Morozov, Artem Babenko
In plenty of machine learning applications, the most relevant items for a particular query should be efficiently extracted, while the relevance function is based on a highly-nonlinear model, e. g., DNNs or GBDTs.
1 code implementation • ICCV 2019 • Stanislav Morozov, Artem Babenko
We tackle the problem of unsupervised visual descriptors compression, which is a key ingredient of large-scale image retrieval systems.
1 code implementation • NeurIPS 2018 • Stanislav Morozov, Artem Babenko
In this paper we address the problem of Maximum Inner Product Search (MIPS) that is currently the computational bottleneck in a large number of machine learning applications.