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IMAGE LABELING DEEP LEARNING FOR MAC GENERATOR
IMAGE LABELING DEEP LEARNING FOR MAC REGISTRATION

Registration Fees (include admission and all course materials): Academia For selection purposes, please note that your application will not be considered without a letter of motivation. The course is limited to 24 participants. Groups 4-6: What worked, what didn’t, what could be done next?ĭiscussion: From Classroom To Core Facility Groups 1-3: What worked, what didn’t, what could be done next? Group activity: Finish practicals, preparation of presentations Real use case presentation: Classificationĭay 5 – Friday 21 January 2022 12:45–13:00 To find out the equivalent time zone in your location, enter Berlin, the programme time and date along with your city into the Time Zone Converter.ĭay 1 – Monday 17 January 2022 12:45 – 13:00 Got something to say? Tweet it with #EMBLDeepLearningĪll times in the programme below are shown as the time in Europe/Berlin. Speakers and Trainers Scientific Organisers

Perform simple quality control on the results.

Train and use a CNN for a bioimage analysis task studied in the course.

Advise users/colleagues in strategies to obtain ground truth.

Understand the fundamentals of machine learning methods suitable for image analysis.

Prerequisites for this workshop are programming skills in Python and ideally Tensorflow, Keras or Pytorch as well as basic knowledge of machine learning theory. This course is aimed at both core facility staff and research scientists. The week-long course in January 2022 will include a few talks, but will mostly be devoted to hands-on work on real data, in small groups of 3-4 participants. The 3 pre-course sessions will have associated homework. We will begin with 3 pre-course sessions (on Friday, 3 December 2021, Friday, 17 December 2021 and on Monday, 10 January 2022) which will bring everyone to a good starting position for a week-long intensive virtual course in January 2022. Participants should provide an outline of one image analysis task they would like to work on during the course. This is a blended learning course with practical and theoretical sessions. The aim of our course is to close this gap and teach the participants – in the most hands-on way possible – to apply deep learning-based methods to their own data and image analysis problems. Despite these advances, training and deployment of such neural networks remains difficult for practitioners of image analysis. Neural networks have been successfully used for image restoration, classification and segmentation, for the detection of objects and characterisation of their morphology, for high-throughput imaging and large-scale processing in 3D.

The advent of deep learning has brought a revolution in the field of computer vision, including most tasks and research questions concerned with microscopy image analysis. We are delighted to announce that the course is going virtual and invite you to join us online. He received his PhD from the University of Leiden, The Netherlands and has been involved in the past with the University of Amsterdam, The Netherlands and the University of Illinois at Urbana-Champaign, USA.EMBL is committed to sharing research advances and sustaining scientific interaction throughout the coronavirus pandemic. Nicu Sebe is a professor in the University of Trento, Italy, where he is leading the research in the areas of multimedia information retrieval and human-computer interaction in computer vision applications. Our solutions score best on diverse benchmarks and on a variety of object categories.

IMAGE LABELING DEEP LEARNING FOR MAC GENERATOR

A generator network models occlusions arising during target motions and combines the appearance extracted from the source image and the motion derived from the driving video. To support complex motions, we use a representation consisting of a set of learned keypoints along with their local affine transformations. To achieve this, we decouple appearance and motion information using a self-supervised formulation. faces, human bodies), our method can be applied to any object of this class. Once trained on a set of videos depicting objects of the same category (e.g. 2) generating videos without using any annotation or prior information about the specific object to animate. In this talk I will present some of our recent achievements adressing these specific aspects: 1) generating facial expressions, e.g., smiles that are different from each other (e.g., spontaneous, tense, etc.) using diversity as the driving force. Video generation consists of generating a video sequence so that an object in a source image is animated according to some external information (a conditioning label or the motion of a driving video). This AI4EU Café will present Nicu Sebe (Professor in the University of Trento, Italy).