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We propose a novel blind super resolution pipeline aimed at improving consistency and reducing artefacts generation.
The concept of catastrophic forgetting has been the foundation of continual learning, however, this phenomenon is only attributed to the generalization capabilities of the neural network. We hypothesize that there is a strong trigonal relationship between Catastrophic Forgetting, Generalization and Robustness.
We explore the regime of supporting multi-domain based multi-modal continial learning frameworks along with proving their robustness to domain targetted/ mode targetted attacks.
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Supported by contributions from: