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5 papers · 1 filter
Unifying Heterogeneous Classifiers with Distillation
Jayakorn Vongkulbhisal, Phongtharin Vinayavekhin, Marco Visentini-Scarzanella
In this paper, we study the problem of unifying knowledge from a set of classifiers with different architectures and target classes into a single classifier, given only a generic s…
Learning to Generate Synthetic Data via Compositing
Shashank Tripathi, Siddhartha Chandra, Amit Agrawal +3
We present a task-aware approach to synthetic data generation. Our framework employs a trainable synthesizer network that is optimized to produce meaningful training samples by ass…
Bag of Freebies for Training Object Detection Neural Networks
Zhi Zhang, Tong He, Hang Zhang +3
Training heuristics greatly improve various image classification model accuracies~\cite{he2018bag}. Object detection models, however, have more complex neural network structures an…
Joint Learning of Set Cardinality and State Distribution
S. Hamid Rezatofighi, Anton Milan, Qinfeng Shi +2
We present a novel approach for learning to predict sets using deep learning. In recent years, deep neural networks have shown remarkable results in computer vision, natural langua…
3D Face Morphable Models "In-the-Wild"
James Booth, Epameinondas Antonakos, Stylianos Ploumpis +3
3D Morphable Models (3DMMs) are powerful statistical models of 3D facial shape and texture, and among the state-of-the-art methods for reconstructing facial shape from single image…