Abstract
Publications home
Learning in a changing environment: memory strategies for streaming learning under distributional shifts.
at
| Riccardo Volpi, Cesar de Souza, Yannis Kalantidis, Diane Larlus, Gregory Rogez |
| Findings of the Workshop on Continual Learning in Computer Vision (CLVISION) at the Conference on Computer Vision and Pattern Recognition (CVPR), virtual event, 25 June 2021 |
| Download |
Most learning algorithms rely on certain assumptions to work properly, namely that a) all training data is available before training, and b) all training data belongs to the same distribution. However, it should be easy to see those assumptions do not always hold true in practice. A robot interacting with the environment may obtain new data samples as time goes by; a SaaS operator may want to improve models which have already been trained and deployed without re-starting training from scratch or revisiting all data samples already seen by the model; or one could want to learn a model on a potentially infinite source of data. In this work, we explore memory-based methods to efficiently train neural networks when training samples are provided in the form of a data stream whose underlying distribution changes in well-distinct domains—on which we desire to perform uniformly well. We show that different memory-update strategies have a deep impact in the efficacy of the learning, addressing the catastrophic forgetting phenomena often associated with a shift in the input domain. We provide a protocol for assessing the characteristics of different strategies and show how choosing them correctly can result in models that are less sensitive to a particular choice of hyper-parameters, such as the learning rate.
Related Content
To make robots autonomous in real-world everyday spaces, they should be able to learn from their interactions within these spaces, how to best execute tasks specified by non-expert users in a safe and reliable way. To do so requires sequential decision-making skills that combine machine learning, adaptive planning and control in uncertain environments as well as solving hard combinatorial optimisation problems. Our research combines expertise in reinforcement learning, computer vision, robotic control, sim2real transfer, large multimodal foundation models and neural combinatorial optimisation to build AI-based architectures and algorithms to improve robot autonomy and robustness when completing everyday complex tasks in constantly changing environments.
VISION
The research we conduct on expressive visual representations is applicable to visual search, object detection, image classification and the automatic extraction of 3D human poses and shapes that can be used for human behavior understanding and prediction, human-robot interaction or even avatar animation. We also extract 3D information from images that can be used for intelligent robot navigation, augmented reality and the 3D reconstruction of objects, buildings or even entire cities.
Our work covers the spectrum from unsupervised to supervised approaches, and from very deep architectures to very compact ones. We’re excited about the promise of big data to bring big performance gains to our algorithms but also passionate about the challenge of working in data-scarce and low-power scenarios.
Furthermore, we believe that a modern computer vision system needs to be able to continuously adapt itself to its environment and to improve itself via lifelong learning. Our driving goal is to use our research to deliver embodied intelligence to our users in robotics, autonomous driving, via phone cameras and any other visual means to reach people wherever they may be.
This web site uses cookies for the site search, to display videos and for aggregate site analytics.
Learn more about these cookies in our privacy notice.
Cookie settings
You may choose which kind of cookies you allow when visiting this website. Click on "Save cookie settings" to apply your choice.
This content is currently blocked. To view the content please either 'Accept social media cookies' or 'Accept all cookies'.
For more information on cookies see our privacy notice.