jan'19 | In March I'll join Google Research in Amsterdam, I'll work with Cristian Sminchisescu in Zurich and Nal Kalchbrenner of the Amsterdam Brain team! I'll remain affiliated with the UvA.
jan'19 | Research on detecting fake videos (from the SAVI project with SRI and IDIAP) featured in MercuryNews and CNN
dec'18 | Two MSc AI projects (for UvA students): extending DeepNCM and encoding context in visual representations. See below for more information
dec'18 | New paper about generating optical flow ground-truth is available on arXiv, work with An, Tushar, Anil, Sezer and Theo
nov'18 | I helped answering the question do we need to be afraid of fake-news for Know-Shit (3FM Tussenuur)
sep'18 | Added information about Applied Machine Learning and Project AI for 2018, see here
sep'18 | I'll lead the "Captioning News Footage" workshop in the ICT with Industy (jan 2019), together with Daan Odijk, see NWO website for more informationWant to join our team? Contact me!
aug'18 | Our paper about Emoji prediction (with Spencer and Cees) is now available under early access on IEEEXplore
jul'18 | Our paper Three for one and one for three: Flow, Segmentation, and Surface Normals
is accepted for oral presentation at BMVC, toghether with An, Anil, and Theo
may'18 | The project Spotting Audio-Visual Inconsistencies with SRI and IDIAP aired in the news this week(s): including
AG Connect (in Dutch), Tech Crunch, CBS news (with the program leader), and (again) AG Connect (in dutch, more in detail, (local pdf copy)).
mar'18 | Two papers accepted for ICLR Workshops: - DeepNCM: Deep Nearest Class Mean Classifiers, with Samantha Guerriero and Barbara Caputo, see openreview and the code on GitHub - IterGANs for Object Rotation, with Ysbrand Galama, see openreview, the extended version on arXiv, and source code on GitHub.
Project AI, Project AI 2 and Project AI 3
Information for the year 2018-2019
Project AI is a 4 week course in blok 6 (june). It is a project course, working in groups of 4 students. More info to follow (april/may).
Project AI 2 and Project AI 3 are individual projects.
Each project has to be approved by the Examinations Board.
Ususally the procedure is as follows:
Usually projects are 6ECTS, for large projects it could be 12 ECTS
Requests take around 8 weeks for approval
After approval, start the project
After completion, the supervisor and examinator will grade your project and hand in the grade at the ESC desk. Note (since Jan 2019):In the current approval form an assesment form is attached. When you have an older form, download the new form and use the provided grading/assessment form.
Note: In total you're entitled to have a maximum of 12 ECTS of project courses (PAI, PAI2, and PAI3) on your study programme!
Teaching (Outdated)
2017 Computer Vision II, MSc AI
2016 Applied Machine Learning, MSc IS (HCM and DS track)
2013-2015 Visual Search Engines, MSc IS (HCM track)
Current Supervision (Outdated)
MSc IS 2017: Daniël Bartolomé-Rojas, Diederik Beker, Michal Kozal, Yu-Ri Tan, Madli Uutma, Bastiaan Waanders, and Aeron Yung
Msc AI 2017: Ysbrand Galema, Thomas Jongstra (finished March'17) and Sébastien Negrijn
BSc AI 2017: Caitlin Lagrand
Teaching & Supervision
Open Projects
MSc AI thesis
Encoding Context in Visual RepresentationsAbstract: The goal of this project is to study how context is encoded in visual representations.
There is evidence that activation patterns in brain regions related to the visual observations are influenced by an external context, eg by music.
So, depending on the style of music, the same visual stimulus yield different activation patterns.
In this project, we aim to reproduce this behavior by training ConvNets with an additional context input.
The goal is to observe to what extend we obtain similar results and how those insights can be used for further understanding of the brain.
This is a joint project with Jorrit Montijn (Netherlands Institute for Neuroscience, Google Scholar).
Prior knowledge:
Followed Computer Vision 1 / Deep Learning
Interest in Machine Learning
Interest in Neuroscience
Willing to work at both NIN and Science Park
Experience with TensorFlow/PyTorch
Large Scale Visual Classification Using Class Means Abstract: The final layer of (almost) any classification network is a soft-max layer.
An alternative is to learn using class prototypes, eg the mean representation of each class.
In this project we explore Deep Nearest Mean Classification for large scale classification.
The starting point is the DeepNCM paper, but the goal is to extend DeepNCM and to explore training on large(r) datasets in the order of 3,000 classes and 10M training images.