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Incremental Class Learning using Variational Autoencoders with Similarity Learning
We show that one does not require stored images (exemplars) for incremental learning with similarity learning.
ConVAEr: Convolutional Variational AutoEncodeRs for incremental similarity learning
We then investigate the viability of generating “embedding” exemplars from a VAE that can protect base knowledge in the intermediate to output layers of the neural networks.
Unique Faces Recognition in Videos
The contribution of this paper is two-fold: to begin, the experiments have established 3-D Convolutional networks and 2-D LSTMs with the contrastive loss on image sequences do not outperform Google/Inception architecture with contrastive loss in top $n$ rank face retrievals with still images.
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