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In Defense of Pseudo-Labeling: An Uncertainty-Aware Pseudo-label Selection Framework for Semi-Supervised Learning
Mamshad Nayeem Rizve, Kevin Duarte, Yogesh S Rawat +1
The recent research in semi-supervised learning (SSL) is mostly dominated by consistency regularization based methods which achieve strong performance. However, they heavily rely o…
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…
Training Faster by Separating Modes of Variation in Batch-normalized Models
Mahdi M. Kalayeh, Mubarak Shah
Batch Normalization (BN) is essential to effectively train state-of-the-art deep Convolutional Neural Networks (CNN). It normalizes inputs to the layers during training using the s…
Task-Agnostic Meta-Learning for Few-shot Learning
Muhammad Abdullah Jamal, Guo-Jun Qi, Mubarak Shah
Meta-learning approaches have been proposed to tackle the few-shot learning problem.Typically, a meta-learner is trained on a variety of tasks in the hopes of being generalizable t…