Environmental Bayesian Optimization of Gerneral-Purpose Convolutional Neural Networks
Lead Research Organisation:
University of Oxford
Department Name: Engineering Science
Abstract
With the advent of Big Data and modern Deep Learning technology, areas such as computer vision are entering a renaissance. Performance on standard problems such as visual object recognition is soaring, and very difficult challenges such as automatic image captioning can now be tackled with good results. Yet, despite the advances in such specific tasks, deep networks are still far beyond general-purposes intelligences such as humans. A key missing feature is the ability of excelling in *complex environments*, where thousands of interrelated tasks need to be solved together. One of the outstanding technical challenges in scaling deep learning to general-purpose cognition systems is large scale optimization in such complex environments. In this project, we propose to develop Bayesian Optimization as a potential solution to this challenge.
Bayesian Optimization has proved indispensable in automatically tuning the hyper-parameters of commonly used machine learning algorithms, but has been under-explored in the training and deployment phases as well. Nevertheless, the strengths of Bayesian optimization make it an ideal candidate in large scale distributed optimization as well as optimization on multiple tasks. We will demonstrate this technology on computer vision applications. Whereas current vision algorithms focus on specific tasks (e.g. recognizing 1000 object categories or reading text), we will learn general-purpose and efficient computer vision systems from different and complementary tasks.
The company Mathworks is involved. This project falls within the EPSRC information and communication technologies research area.
Bayesian Optimization has proved indispensable in automatically tuning the hyper-parameters of commonly used machine learning algorithms, but has been under-explored in the training and deployment phases as well. Nevertheless, the strengths of Bayesian optimization make it an ideal candidate in large scale distributed optimization as well as optimization on multiple tasks. We will demonstrate this technology on computer vision applications. Whereas current vision algorithms focus on specific tasks (e.g. recognizing 1000 object categories or reading text), we will learn general-purpose and efficient computer vision systems from different and complementary tasks.
The company Mathworks is involved. This project falls within the EPSRC information and communication technologies research area.
Organisations
People |
ORCID iD |
Sylvestre-Alvise Rebuffi (Student) |
Publications
Rebuffi S-A
(2017)
Learning multiple visual domains with residual adapters
Rebuffi S
(2018)
Efficient Parametrization of Multi-domain Deep Neural Networks
Rebuffi S-A
(2020)
There and Back Again: Revisiting Backpropagation Saliency Methods
Studentship Projects
Project Reference | Relationship | Related To | Start | End | Student Name |
---|---|---|---|---|---|
EP/N509711/1 | 30/09/2016 | 29/09/2021 | |||
1802210 | Studentship | EP/N509711/1 | 30/09/2016 | 29/09/2020 | Sylvestre-Alvise Rebuffi |
Description | We published several papers in the area of Computer Vision. Two works were about how to reuse the knowledge learned by a neural network and apply it on another domain/topic. In another work, we proposed a novel class discovery method which based on the knowledge of several classes of objects can discover and cluster new data in new classes without human supervision. |
Exploitation Route | Like several applied AI methods, it can have direct applications for industry: communication, medical, ... Our methods are easy to implement and their code is released. |
Sectors | Aerospace Defence and Marine Digital/Communication/Information Technologies (including Software) Healthcare Other |
Description | Spotlight presentation at NeurIPS conference |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Postgraduate students |
Results and Impact | My paper was selected for a spotlight presentation at the NeurIPS conference. This presentation lasted 5 minutes in front of an audience of researchers in the main hall of the conference. |
Year(s) Of Engagement Activity | 2018 |