Improving ClusterGAN Using Self-AugmentedInformation Maximization of Disentangling LatentSpaces

The Latent Space Clustering in Generative adversarial networks (ClusterGAN) method has been successful with high-dimensional data. However, the method assumes uniformlydistributed priors during the generation of modes, which isa restrictive assumption in real-world data and cause loss ofdiversity in the generated modes. In this paper, we proposeself-augmentation information maximization improved Clus-terGAN (SIMI-ClusterGAN) to learn the distinctive priorsfrom the data. The proposed SIMI-ClusterGAN consists offour deep neural networks: self-augmentation prior network,generator, discriminator and clustering inference autoencoder.The proposed method has been validated using seven bench-mark data sets and has shown improved performance overstate-of-the art methods. To demonstrate the superiority ofSIMI-ClusterGAN performance on imbalanced dataset, wehave discussed two imbalanced conditions on MNIST datasetswith one-class imbalance and three classes imbalanced cases.The results highlight the advantages of SIMI-ClusterGAN.

Mixture of Spectral Generative Adversarial Networks for Imbalanced Hyperspectral Image Classification

We propose a three-player spectral generative adversarial network (GAN) architecture to afford GAN with the ability to manage minority classes under imbalance conditions. A class-dependent mixture generator spectral GAN (MGSGAN) has been developed to force generated samples remain within the domain of the actual distribution of the data. MGSGAN is able to generate minority classes even when the imbalance ratio of majority to minority classes is high. A classifier based on lower features is adopted with a sequential discriminator to form a three-player GAN game. The generator networks perform data augmentation to improve the classifiers performance. The proposed method has been validated through two hyperspectral images datasets and compared with state-of-the-art methods under two class-imbalance settings corresponding to real data distributions.

Q-Learning with Differential Entropy of Q-Tables

It is well-known that information loss can occur in the classic and simple Q-learning algorithm. Entropy-based policy search methods were introduced to replace Q-learning and to design algorithms that are more robust against information loss. We conjecture that the reduction in performance during prolonged training sessions of Q-learning is caused by a loss of information, which is non-transparent when only examining the cumulative reward without changing the Q-learning algorithm itself. We introduce Differential Entropy of Q-tables (DE-QT) as an external information loss detector to the Q-learning algorithm. The behaviour of DE-QT over training episodes is analyzed to find an appropriate stopping criterion during training. The results reveal that DE-QT can detect the most appropriate stopping point, where a balance between a high success rate and a high efficiency is met for classic Q-Learning algorithm.

Towards Interpretable Neural Networks: An Exact Transformation to Multi-Class Multivariate Decision Trees

Artificial neural networks (ANNs) are commonly labelled as black-boxes, lacking interpretability. This hinders human understanding of ANNs behaviors. A need exists to generate a meaningful sequential logic for the production of a specific output. Decision trees exhibit better interpretability and expressive power due to their representation language and the existence of efficient algorithms to generate rules. Growing a decision tree based on the available data could produce larger than necessary trees or trees that do not generalise well. In this paper, we introduce two novel multivariate decision tree (MDT) algorithms for rule extraction from an ANN: an Exact-Convertible Decision Tree (EC-DT) and an Extended C-Net algorithm to transform a neural network with Rectified Linear Unit activation functions into a representative tree which can be used to extract multivariate rules for reasoning. While the EC-DT translates the ANN in a layer-wise manner to represent exactly the decision boundaries implicitlylearned by the hidden layers of the network, the Extended C-Net inherits the decompositional approach from EC-DT and combines with a C5 tree learning algorithm to construct the decision rules. The results suggest that while EC-DT is superior in preserving the structure and the accuracy of ANN, Extended C-Net generates the most compact and highly effective trees from ANN. Both proposed MDT algorithms generate rules including combinations of multiple attributes for precise interpretation of decision-making processes.