NeuCrowd: neural sampling network for representation learning with crowdsourced labels

Learning Attributes from the Crowdsourced Relative Labels

Finding semantic attributes to describe related concepts is typically a hard problem. The commonly used attributes in most fields are designed by domain experts, which is expensive and time-consuming. In this paper we propose an efficient method to learn human comprehensible attributes with crowdsourcing. We first design an analogical interface to collect relative labels from the crowds. Then w...

Learning classifier systems with neural network representation

Learning Document Image Features With SqueezeNet Convolutional Neural Network

The classification of various document images is considered an important step towards building a modern digital library or office automation system. Convolutional Neural Network (CNN) classifiers trained with backpropagation are considered to be the current state of the art model for this task. However, there are two major drawbacks for these classifiers: the huge computational power demand for...

Neural Simpletrons - Minimalistic Probabilistic Networks for Learning With Few Labels

Classifiers for the semi-supervised setting often combine strong supervised models with additional learning objectives to make use of unlabeled data. This results in powerful though very complex models that are hard to train and that demand additional labels for optimal parameter tuning, which are often not given when labeled data is very sparse. We here study a minimalistic multi-layer generat...

Learning a Neural-network-based Representation for Open Set Recognition

Open set recognition problems exist in many domains. For example in security, new malware classes emerge regularly; therefore malware classi€cation systems need to identify instances from unknown classes in addition to discriminating between known classes. In this paper we present a neural network based representation for addressing the open set recognition problem. In this representation insta...