Search Results for author: Ksenia Konyushkova

Found 11 papers, 5 papers with code

Active Offline Policy Selection

1 code implementation NeurIPS 2021 Ksenia Konyushkova, Yutian Chen, Tom Le Paine, Caglar Gulcehre, Cosmin Paduraru, Daniel J Mankowitz, Misha Denil, Nando de Freitas

We use multiple benchmarks, including real-world robotics, with a large number of candidate policies to show that the proposed approach improves upon state-of-the-art OPE estimates and pure online policy evaluation.

Offline Learning from Demonstrations and Unlabeled Experience

no code implementations27 Nov 2020 Konrad Zolna, Alexander Novikov, Ksenia Konyushkova, Caglar Gulcehre, Ziyu Wang, Yusuf Aytar, Misha Denil, Nando de Freitas, Scott Reed

Behavior cloning (BC) is often practical for robot learning because it allows a policy to be trained offline without rewards, by supervised learning on expert demonstrations.

Continuous Control Imitation Learning +1

Learning Intelligent Dialogs for Bounding Box Annotation

1 code implementation CVPR 2018 Ksenia Konyushkova, Jasper Uijlings, Christoph Lampert, Vittorio Ferrari

We demonstrate that (1) our agents are able to learn efficient annotation strategies in several scenarios, automatically adapting to the image difficulty, the desired quality of the boxes, and the detector strength; (2) in all scenarios the resulting annotation dialogs speed up annotation compared to manual box drawing alone and box verification alone, while also outperforming any fixed combination of verification and drawing in most scenarios; (3) in a realistic scenario where the detector is iteratively re-trained, our agents evolve a series of strategies that reflect the shifting trade-off between verification and drawing as the detector grows stronger.

Geometry in Active Learning for Binary and Multi-class Image Segmentation

no code implementations29 Jun 2016 Ksenia Konyushkova, Raphael Sznitman, Pascal Fua

Our approach combines geometric smoothness priors in the image space with more traditional uncertainty measures to estimate which pixels or voxels are the most informative, and thus should to be annotated next.

Active Learning Semantic Segmentation

God(s) Know(s): Developmental and Cross-Cultural Patterns in Children Drawings

no code implementations11 Nov 2015 Ksenia Konyushkova, Nikolaos Arvanitopoulos, Zhargalma Dandarova Robert, Pierre-Yves Brandt, Sabine Süsstrunk

This paper introduces a novel approach to data analysis designed for the needs of specialists in psychology of religion.

Introducing Geometry in Active Learning for Image Segmentation

no code implementations ICCV 2015 Ksenia Konyushkova, Raphael Sznitman, Pascal Fua

We propose an Active Learning approach to training a segmentation classifier that exploits geometric priors to streamline the annotation process in 3D image volumes.

Active Learning Semantic Segmentation

Cannot find the paper you are looking for? You can Submit a new open access paper.