Multi-layer random forests with auto-context for object detection and pose estimation

Deep neural networks are multi-layer architectures of neural networks used in machine learning. In recent years, training such systems through Deep Learning techniques has shown remarkable improvements over more traditional techniques in the domains of image-based classification and object category recognition. However, a well-known downside of Deep Learning is that generally, for training these very-high-dimensional systems, a very large amount of training samples is required. It is a common understanding that the multi-level structure of data processing that is learned and applied may be a critical factor for the success story of deep neural networks. Highly task-optimized representations may be gradually built through a sequence of transformations that can abstract and hence generalize from the specific data instances. In this Master Thesis, the aspect of deep multi-layered data transformation will be transferred from neural networks onto a very different learning scheme, the Random Forests. The latter is a highly competitive and general-purpose method for classification and regression. Even large Random Forests usually do not have the high amount of parameters to optimize during training, and the number of hyper-parameters and architectural varieties is much lower than for the deep neural networks. Training with a much smaller amount of samples is hence possible. Some variants of a layered architecture of Random Forests is investigated here, and different styles of training each forest is tried. The specific problem domain considered here is object detection and pose estimation from RGB-D images. Hence, the forest output is used by the pose hypothesis scoring function and the poses are optimized using RANSAC-based scheme. Experiments on the pose annotated Hinterstoisser and T-LESS datasets prove the performance of the multi-layer random forests architecture

A Hybrid End-to-End Spatio-Temporal Attention Neural Network with Graph-Smooth Signals for EEG Emotion Recognition

Recently, physiological data such as electroencephalography (EEG) signals have attracted significant attention in affective computing. In this context, the main goal is to design an automated model that can assess emotional states. Lately, deep neural networks have shown promising performance in emotion recognition tasks. However, designing a deep architecture that can extract practical information from raw data is still a challenge. Here, we introduce a deep neural network that acquires interpretable physiological representations by a hybrid structure of spatio-temporal encoding and recurrent attention network blocks. Furthermore, a preprocessing step is applied to the raw data using graph signal processing tools to perform graph smoothing in the spatial domain. We demonstrate that our proposed architecture exceeds state-of-the-art results for emotion classification on the publicly available DEAP dataset. To explore the generality of the learned model, we also evaluate the performance of our architecture towards transfer learning (TL) by transferring the model parameters from a specific source to other target domains. Using DEAP as the source dataset, we demonstrate the effectiveness of our model in performing cross-modality TL and improving emotion classification accuracy on DREAMER and the Emotional English Word (EEWD) datasets, which involve EEG-based emotion classification tasks with different stimuli

Latent Multi-task Architecture Learning

Multi-task learning (MTL) allows deep neural networks to learn from related tasks by sharing parameters with other networks. In practice, however, MTL involves searching an enormous space of possible parameter sharing architectures to find (a) the layers or subspaces that benefit from sharing, (b) the appropriate amount of sharing, and (c) the appropriate relative weights of the different task losses. Recent work has addressed each of the above problems in isolation. In this work we present an approach that learns a latent multi-task architecture that jointly addresses (a)--(c). We present experiments on synthetic data and data from OntoNotes 5.0, including four different tasks and seven different domains. Our extension consistently outperforms previous approaches to learning latent architectures for multi-task problems and achieves up to 15% average error reductions over common approaches to MTL.Comment: To appear in Proceedings of AAAI 201