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Found 7 papers, 1 papers with code
Fiducial Focus Augmentation for Facial Landmark Detection
To effectively utilize the newly proposed augmentation technique, we employ a Siamese architecture-based training mechanism with a Deep Canonical Correlation Analysis (DCCA)-based loss to achieve collective learning of high-level feature representations from two different views of the input images.
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Facial Landmark Detection
on WFLW
Class-Incremental Learning with Cross-Space Clustering and Controlled Transfer
This allows the model to learn classes in such a way that it maximizes positive forward transfer from similar prior classes, thus increasing plasticity, and minimizes negative backward transfer on dissimilar prior classes, whereby strengthening stability.
Learning Modular Structures That Generalize Out-of-Distribution
generalization remains to be a key challenge for real-world machine learning systems.
Retrospective Loss: Looking Back to Improve Training of Deep Neural Networks
Deep neural networks (DNNs) are powerful learning machines that have enabled breakthroughs in several domains.
Retrospection: Leveraging the Past for Efficient Training of Deep Neural Networks
Deep neural networks are powerful learning machines that have enabled breakthroughs in several domains.
Training Autoencoders by Alternating Minimization
We present DANTE, a novel method for training neural networks, in particular autoencoders, using the alternating minimization principle.
DAiSEE: Towards User Engagement Recognition in the Wild
We introduce DAiSEE, the first multi-label video classification dataset comprising of 9068 video snippets captured from 112 users for recognizing the user affective states of boredom, confusion, engagement, and frustration in the wild.
