Thesis : Frugal Learning with Deep Generative Networks for Visual Scene RecognitionPhD school thesis
- The design of new GANs/VAEs for image generation, augmentation and replay.
- The disentanglement and interpretation of different factors in these generative models including semantics, appearances and dynamics of the visual contents.
- The inclusion of these generative models in a whole framework that achieves continual learning while also handling the challenging issue of catastrophic forgetting.
- These networks should be designed in order to run, not only on standard GPUs, but also on edge devices including mobile phones and connected objects endowed with low computational and energy resources.
- Applications include continual object detection, image classification and segmentation in still and video sequences.
This PhD research project has been submitted for a funding request to “Ecole Doctorale d‘Informatique, Télécommunication et d‘Electronique (EDITE)”. The PhD candidate selected by the project leader will therefore participate in the project selection process (including a file and an interview) to obtain funding.
Contact :Hichem Sahbi