3 accepted papers
Unsupervised anomaly detection is a challenging problem, where the aim is to detect irregular data instances. Interestingly, generative models can learn data distribution, and thus have been proposed for anomaly detection. In this direction, the variational autoencoder (VAE) is popular, as it enforc…
Multimodal learning leverages data from different modalities to improve the performance of a trained model. Typically, latent representations extracted from multimodal data are provided via direct feature fusion for end-to-end training of a deep neural network towards a specific task. However, the i…
Deep neural networks inherently have large representational power for approximating complex target functions. However, models based on rectified linear units can suffer reduction in representation capacity due to dead units. Moreover, approximating very deep networks trained with dropout at test tim…