Search Results for author: Stanislav Morozov

Found 7 papers, 6 papers with code

On Self-Supervised Image Representations for GAN Evaluation

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.

Contrastive Learning General Classification

Object Segmentation Without Labels with Large-Scale Generative Models

1 code implementation8 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.

Image Classification Object +5

Neural Oblivious Decision Ensembles for Deep Learning on Tabular Data

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.

BIG-bench Machine Learning Representation Learning

Relevance Proximity Graphs for Fast Relevance Retrieval

1 code implementation19 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.


Unsupervised Neural Quantization for Compressed-Domain Similarity Search

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.

Image Retrieval Quantization +1

Non-metric Similarity Graphs for Maximum Inner Product Search

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.

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