Bayesian Deep Neural Networks for Supervised Learning of Single-View Depth

Bayesian Incremental Learning for Deep Neural Networks

In industrial machine learning pipelines, data often arrive in parts. Particularly in the case of deep neural networks, it may be too expensive to train the model from scratch each time, so one would rather use a previously learned model and the new data to improve performance. However, deep neural networks are prone to getting stuck in a suboptimal solution when trained on only new data as com...

Bayesian Methods for Supervised Neural Networks

Deep Bayesian Active Semi-Supervised Learning

In many applications the process of generating label information is expensive and time consuming. We present a new method that combines active and semi-supervised deep learning to achieve high generalization performance from a deep convolutional neural network with as few known labels as possible. In a setting where a small amount of labeled data as well as a large amount of unlabeled data is a...

Deep Learning for Single-View Instance Recognition

Deep learning methods have typically been trained on large datasets in which many training examples are available. However, many real-world product datasets have only a small number of images available for each product. We explore the use of deep learning methods for recognizing object instances when we have only a single training example per class. We show that feedforward neural networks outp...

Multi-view, Multi-label Learning with Deep Neural Networks

Deep learning is a popular technique in modern online and offline services. Deep neural network based learning systems have made groundbreaking progress in model size, training and inference speed, and expressive power in recent years, but to tailor the model to specific problems and exploit data and problem structures is still an ongoing research topic. We look into two types of deep ‘‘multi-’...