Search Results for author: Sergey Zagoruyko

Found 16 papers, 13 papers with code

Safe Real-World Autonomous Driving by Learning to Predict and Plan with a Mixture of Experts

no code implementations3 Nov 2022 Stefano Pini, Christian S. Perone, Aayush Ahuja, Ana Sofia Rufino Ferreira, Moritz Niendorf, Sergey Zagoruyko

The code for training and testing our model on a public prediction dataset and the video of the road test are available at https://woven. mobi/safepathnet

Autonomous Driving Navigate

CW-ERM: Improving Autonomous Driving Planning with Closed-loop Weighted Empirical Risk Minimization

1 code implementation5 Oct 2022 Eesha Kumar, Yiming Zhang, Stefano Pini, Simon Stent, Ana Ferreira, Sergey Zagoruyko, Christian S. Perone

The imitation learning of self-driving vehicle policies through behavioral cloning is often carried out in an open-loop fashion, ignoring the effect of actions to future states.

Autonomous Driving Imitation Learning

Monte-Carlo Tree Search for Efficient Visually Guided Rearrangement Planning

2 code implementations23 Apr 2019 Yann Labbé, Sergey Zagoruyko, Igor Kalevatykh, Ivan Laptev, Justin Carpentier, Mathieu Aubry, Josef Sivic

We address the problem of visually guided rearrangement planning with many movable objects, i. e., finding a sequence of actions to move a set of objects from an initial arrangement to a desired one, while relying on visual inputs coming from an RGB camera.

Exploring Weight Symmetry in Deep Neural Networks

1 code implementation28 Dec 2018 Xu Shell Hu, Sergey Zagoruyko, Nikos Komodakis

We propose several ways to impose local symmetry in recurrent and convolutional neural networks, and show that our symmetry parameterizations satisfy universal approximation property for single hidden layer networks.

Language Modelling

Compressing the Input for CNNs with the First-Order Scattering Transform

1 code implementation ECCV 2018 Edouard Oyallon, Eugene Belilovsky, Sergey Zagoruyko, Michal Valko

We study the first-order scattering transform as a candidate for reducing the signal processed by a convolutional neural network (CNN).

General Classification Translation

Scattering Networks for Hybrid Representation Learning

1 code implementation17 Sep 2018 Edouard Oyallon, Sergey Zagoruyko, Gabriel Huang, Nikos Komodakis, Simon Lacoste-Julien, Matthew Blaschko, Eugene Belilovsky

In particular, by working in scattering space, we achieve competitive results both for supervised and unsupervised learning tasks, while making progress towards constructing more interpretable CNNs.

Representation Learning

DiracNets: Training Very Deep Neural Networks Without Skip-Connections

3 code implementations1 Jun 2017 Sergey Zagoruyko, Nikos Komodakis

Deep neural networks with skip-connections, such as ResNet, show excellent performance in various image classification benchmarks.

Image Classification

Scaling the Scattering Transform: Deep Hybrid Networks

2 code implementations ICCV 2017 Edouard Oyallon, Eugene Belilovsky, Sergey Zagoruyko

Combining scattering networks with a modern ResNet, we achieve a single-crop top 5 error of 11. 4% on imagenet ILSVRC2012, comparable to the Resnet-18 architecture, while utilizing only 10 layers.

Image Classification

Wide Residual Networks

71 code implementations23 May 2016 Sergey Zagoruyko, Nikos Komodakis

Deep residual networks were shown to be able to scale up to thousands of layers and still have improving performance.

Image Classification

A MultiPath Network for Object Detection

1 code implementation7 Apr 2016 Sergey Zagoruyko, Adam Lerer, Tsung-Yi Lin, Pedro O. Pinheiro, Sam Gross, Soumith Chintala, Piotr Dollár

To address these challenges, we test three modifications to the standard Fast R-CNN object detector: (1) skip connections that give the detector access to features at multiple network layers, (2) a foveal structure to exploit object context at multiple object resolutions, and (3) an integral loss function and corresponding network adjustment that improve localization.

Instance Segmentation Object +2

Learning to Compare Image Patches via Convolutional Neural Networks

1 code implementation CVPR 2015 Sergey Zagoruyko, Nikos Komodakis

In this paper we show how to learn directly from image data (i. e., without resorting to manually-designed features) a general similarity function for comparing image patches, which is a task of fundamental importance for many computer vision problems.

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