Search Results for author: Ozan Sener

Found 25 papers, 14 papers with code

RanDumb: A Simple Approach that Questions the Efficacy of Continual Representation Learning

1 code implementation13 Feb 2024 Ameya Prabhu, Shiven Sinha, Ponnurangam Kumaraguru, Philip H. S. Torr, Ozan Sener, Puneet K. Dokania

Our investigation is both surprising and alarming as it questions our understanding of how to effectively design and train models that require efficient continual representation learning, and necessitates a principled reinvestigation of the widely explored problem formulation itself.

Continual Learning Representation Learning

Domain Generalization without Excess Empirical Risk

no code implementations30 Aug 2023 Ozan Sener, Vladlen Koltun

To solve the proposed optimization problem, we demonstrate an exciting connection to rate-distortion theory and utilize its tools to design an efficient method.

Domain Generalization

Generalized Sum Pooling for Metric Learning

1 code implementation ICCV 2023 Yeti Z. Gurbuz, Ozan Sener, A. Aydin Alatan

GSP improves GAP with two distinct abilities: i) the ability to choose a subset of semantic entities, effectively learning to ignore nuisance information, and ii) learning the weights corresponding to the importance of each entity.

Metric Learning

Simulation-based Inference for Cardiovascular Models

no code implementations26 Jul 2023 Antoine Wehenkel, Jens Behrmann, Andrew C. Miller, Guillermo Sapiro, Ozan Sener, Marco Cuturi, Jörn-Henrik Jacobsen

Over the past decades, hemodynamics simulators have steadily evolved and have become tools of choice for studying cardiovascular systems in-silico.

Online Continual Learning Without the Storage Constraint

1 code implementation16 May 2023 Ameya Prabhu, Zhipeng Cai, Puneet Dokania, Philip Torr, Vladlen Koltun, Ozan Sener

In this paper, we target such applications, investigating the online continual learning problem under relaxed storage constraints and limited computational budgets.

Continual Learning

Generalizing Gaussian Smoothing for Random Search

1 code implementation27 Nov 2022 Katelyn Gao, Ozan Sener

Gaussian smoothing (GS) is a derivative-free optimization (DFO) algorithm that estimates the gradient of an objective using perturbations of the current parameters sampled from a standard normal distribution.

Improving information retention in large scale online continual learning

no code implementations12 Oct 2022 Zhipeng Cai, Vladlen Koltun, Ozan Sener

The typical approach to address information retention (the ability to retain previous knowledge) is keeping a replay buffer of a fixed size and computing gradients using a mixture of new data and the replay buffer.

Continual Learning

MSeg: A Composite Dataset for Multi-domain Semantic Segmentation

2 code implementations CVPR 2020 John Lambert, Zhuang Liu, Ozan Sener, James Hays, Vladlen Koltun

We adopt zero-shot cross-dataset transfer as a benchmark to systematically evaluate a model's robustness and show that MSeg training yields substantially more robust models in comparison to training on individual datasets or naive mixing of datasets without the presented contributions.

Computational Efficiency Instance Segmentation +3

Online Continual Learning with Natural Distribution Shifts: An Empirical Study with Visual Data

1 code implementation ICCV 2021 Zhipeng Cai, Ozan Sener, Vladlen Koltun

We argue that "online" continual learning, where data is a single continuous stream without task boundaries, enables evaluating both information retention and online learning efficacy.

Continual Learning

Modeling and Optimization Trade-off in Meta-learning

1 code implementation NeurIPS 2020 Katelyn Gao, Ozan Sener

By searching for shared inductive biases across tasks, meta-learning promises to accelerate learning on novel tasks, but with the cost of solving a complex bilevel optimization problem.

Bilevel Optimization Meta-Learning +1

Drinking from a Firehose: Continual Learning with Web-scale Natural Language

1 code implementation18 Jul 2020 Hexiang Hu, Ozan Sener, Fei Sha, Vladlen Koltun

Collectively, the POLL problem setting, the Firehose datasets, and the ConGraD algorithm enable a complete benchmark for reproducible research on web-scale continual learning.

Continual Learning

Hausdorff Dimension, Heavy Tails, and Generalization in Neural Networks

1 code implementation NeurIPS 2020 Umut Şimşekli, Ozan Sener, George Deligiannidis, Murat A. Erdogdu

Despite its success in a wide range of applications, characterizing the generalization properties of stochastic gradient descent (SGD) in non-convex deep learning problems is still an important challenge.

Generalization Bounds

Multi-Task Learning as Multi-Objective Optimization

5 code implementations NeurIPS 2018 Ozan Sener, Vladlen Koltun

These algorithms are not directly applicable to large-scale learning problems since they scale poorly with the dimensionality of the gradients and the number of tasks.

Depth Estimation General Classification +7

Generalizing to Unseen Domains via Adversarial Data Augmentation

2 code implementations NeurIPS 2018 Riccardo Volpi, Hongseok Namkoong, Ozan Sener, John Duchi, Vittorio Murino, Silvio Savarese

Only using training data from a single source distribution, we propose an iterative procedure that augments the dataset with examples from a fictitious target domain that is "hard" under the current model.

Data Augmentation Semantic Segmentation

Deep Learning under Privileged Information Using Heteroscedastic Dropout

1 code implementation CVPR 2018 John Lambert, Ozan Sener, Silvio Savarese

This is what the Learning Under Privileged Information (LUPI) paradigm endeavors to model by utilizing extra knowledge only available during training.

Image Classification Machine Translation +1

Learning Transferrable Representations for Unsupervised Domain Adaptation

no code implementations NeurIPS 2016 Ozan Sener, Hyun Oh Song, Ashutosh Saxena, Silvio Savarese

Supervised learning with large scale labelled datasets and deep layered models has caused a paradigm shift in diverse areas in learning and recognition.

Object Recognition Unsupervised Domain Adaptation

3D Semantic Parsing of Large-Scale Indoor Spaces

no code implementations CVPR 2016 Iro Armeni, Ozan Sener, Amir R. Zamir, Helen Jiang, Ioannis Brilakis, Martin Fischer, Silvio Savarese

In this paper, we propose a method for semantic parsing the 3D point cloud of an entire building using a hierarchical approach: first, the raw data is parsed into semantically meaningful spaces (e. g. rooms, etc) that are aligned into a canonical reference coordinate system.

Semantic Parsing

Unsupervised Semantic Action Discovery from Video Collections

no code implementations11 May 2016 Ozan Sener, Amir Roshan Zamir, Chenxia Wu, Silvio Savarese, Ashutosh Saxena

Our method can also provide a textual description for each of the identified semantic steps and video segments.

Watch-n-Patch: Unsupervised Learning of Actions and Relations

no code implementations11 Mar 2016 Chenxia Wu, Jiemi Zhang, Ozan Sener, Bart Selman, Silvio Savarese, Ashutosh Saxena

For evaluation, we contribute a new challenging RGB-D activity video dataset recorded by the new Kinect v2, which contains several human daily activities as compositions of multiple actions interacting with different objects.

Action Segmentation Clustering

Unsupervised Transductive Domain Adaptation

no code implementations10 Feb 2016 Ozan Sener, Hyun Oh Song, Ashutosh Saxena, Silvio Savarese

We incorporate the domain shift and the transductive target inference into our framework by jointly solving for an asymmetric similarity metric and the optimal transductive target label assignment.

Object Recognition Unsupervised Domain Adaptation

Unsupervised Semantic Parsing of Video Collections

no code implementations ICCV 2015 Ozan Sener, Amir Zamir, Silvio Savarese, Ashutosh Saxena

The proposed method is capable of providing a semantic "storyline" of the video composed of its objective steps.

Unsupervised semantic parsing

RoboBrain: Large-Scale Knowledge Engine for Robots

no code implementations1 Dec 2014 Ashutosh Saxena, Ashesh Jain, Ozan Sener, Aditya Jami, Dipendra K. Misra, Hema S. Koppula

In this paper we introduce a knowledge engine, which learns and shares knowledge representations, for robots to carry out a variety of tasks.

Efficient MRF Energy Propagation for Video Segmentation via Bilateral Filters

no code implementations22 Jan 2013 Ozan Sener, Kemal Ugur, A. Aydin Alatan

Depending on the application, automatic or interactive methods are desired; however, regardless of the application type, efficient computation of video object segmentation is crucial for time-critical applications; specifically, mobile and interactive applications require near real-time efficiencies.

Object Segmentation +4

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