Sort by:

• Highly Influential Citations
• Citations
• Acceleration
• Velocity
• Recency

Learn More

Wide Residual Networks

• Sergey Zagoruyko, Nikos Komodakis
• BMVC
• 2016

Deep residual networks were shown to be able to scale up to thousands of layers and still have improving performance. However, each fraction of a percent of improved accuracy costs nearly doubling… (More)

Learning to compare image patches via convolutional neural networks

• Sergey Zagoruyko, Nikos Komodakis
• 2015 IEEE Conference on Computer Vision and…
• 2015

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… (More)

Paying More Attention to Attention: Improving the Performance of Convolutional Neural Networks via Attention Transfer

• Sergey Zagoruyko, Nikos Komodakis
• ICLR 2017
• 2017

Attention plays a critical role in human visual experience. Furthermore, it has recently been demonstrated that attention can also play an important role in the context of applying artificial neural… (More)

A MultiPath Network for Object Detection

• Sergey Zagoruyko, Adam Lerer, +4 authors Piotr Dollár
• BMVC
• 2016

The recent COCO object detection dataset presents several new challenges for object detection. In particular, it contains objects at a broad range of scales, less prototypical images, and requires… (More)

A MRF shape prior for facade parsing with occlusions

• Mateusz Kozinski, Raghudeep Gadde, Sergey Zagoruyko, Guillaume Obozinski, Renaud Marlet
• 2015 IEEE Conference on Computer Vision and…
• 2015

We present a new shape prior formalism for the segmentation of rectified facade images. It combines the simplicity of split grammars with unprecedented expressive power: the capability of encoding… (More)

Scaling the Scattering Transform: Deep Hybrid Networks

• Edouard Oyallon, Eugene Belilovsky, Sergey Zagoruyko
• 2017 IEEE International Conference on Computer…
• 2017

We use the scattering network as a generic and fixed initialization of the first layers of a supervised hybrid deep network. We show that early layers do not necessarily need to be learned, providing… (More)

DiracNets: Training Very Deep Neural Networks Without Skip-Connections

• Sergey Zagoruyko, Nikos Komodakis
• ArXiv
• 2017

Deep neural networks with skip-connections, such as ResNet, show excellent performance in various image classification benchmarks. It is though observed that the initial motivation behind them… (More)

Deep compare: A study on using convolutional neural networks to compare image patches

• Sergey Zagoruyko, Nikos Komodakis
• 2017

BENCHMARKING DEEP LEARNING FRAMEWORKS FOR THE CLASSIFICATION OF VERY HIGH RESOLUTION SATELLITE MULTISPECTRAL DATA

• Maria Papadomanolaki, Maria Vakalopoulou, Sergey Zagoruyko, Konstantinos Karantzalos
• 2016

In this paper we evaluated deep-learning frameworks based on Convolutional Neural Networks for the accurate classification of multispectral remote sensing data. Certain state-of-the-art models have… (More)

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

• Edouard Oyallon, Eugene Belilovsky, Sergey Zagoruyko, Michal Valko
• ECCV
• 2018

We study the first-order scattering transform as a candidate for reducing the signal processed by a convolutional neural network (CNN). We show theoretical and empirical evidence that in the case of… (More)

• ‹
• 1
• 2
• ›