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Modulate Your Spectrum in Self-Supervised Learning
Xi Weng, Yunhao Ni, Tengwei Song +5
Whitening loss offers a theoretical guarantee against feature collapse in self-supervised learning (SSL) with joint embedding architectures. Typically, it involves a hard whitening…
Guidance Through Surrogate: Towards a Generic Diagnostic Attack
Muzammal Naseer, Salman Khan, Fatih Porikli +1
Adversarial training is an effective approach to make deep neural networks robust against adversarial attacks. Recently, different adversarial training defenses are proposed that n…
Meta-learning the Learning Trends Shared Across Tasks
Jathushan Rajasegaran, Salman Khan, Munawar Hayat +2
Meta-learning stands for 'learning to learn' such that generalization to new tasks is achieved. Among these methods, Gradient-based meta-learning algorithms are a specific sub-clas…
iTAML: An Incremental Task-Agnostic Meta-learning Approach
Jathushan Rajasegaran, Salman Khan, Munawar Hayat +2
Humans can continuously learn new knowledge as their experience grows. In contrast, previous learning in deep neural networks can quickly fade out when they are trained on a new ta…