What works best for self-supervised learning & why?

Lead Research Organisation: University College London
Department Name: Computer Science


Recently it was shown that a network trained to estimate how much natural images had been rotated from their canonical orientation (self-supervised training) developed a rich representation of image structure. If the network was then fine-tuned for object class recognition (supervised training) using semantically-labelled data, it was able to achieve good performance with a fraction of the data needed had it been trained in supervised manner from the start. Self-supervised training thus offers a route to high performing networks when data is scarce. Other image manipulations that could be used to drive self-supervised learning include:
non-lossy - contrast inversion; hue rotation; warping
lossy - masking; bit reduction; noisy perturbation; spectral whitening; grayscaling
This PhD will develop and apply a battery of assessments for methods of self-supervised learning, and develop and test a theoretical explanation for what works best, possibly relating the explanation to existing neuroscience concepts such as predictive coding.


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Studentship Projects

Project Reference Relationship Related To Start End Student Name
EP/S021566/1 01/04/2019 30/09/2027
2250955 Studentship EP/S021566/1 23/09/2019 22/09/2023 Augustine Mavor-Parker