Konstantinos Bousmalis

Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks

Collecting well-annotated image datasets to train modern machine learning algorithms is prohibitively expensive for many tasks. One appealing alternative is rendering synthetic data where ground-truth annotations are generated automatically. Unfortunately, models trained purely on rendered images fail to generalize to real images. To address this shortcoming, prior work introduced unsupervised domain adaptation algorithms that have tried to either map representations between the two domains, or learn to extract features that are domain-invariant. In this work, we approach the problem in a new light by learning in an unsupervised manner a transformation in the pixel space from one domain to the other. Our generative adversarial network (GAN)-based method adapts source-domain images to appear as if drawn from the target domain. Our approach not only produces plausible samples, but also outperforms the state-of-the-art on a number of unsupervised domain adaptation scenarios by large margins. Finally, we demonstrate that the adaptation process generalizes to object classes unseen during training.

Spotting agreement and disagreement: A survey of nonverbal audiovisual cues and tools

While detecting and interpreting temporal patterns of non-verbal behavioral cues in a given context is a natural and often unconscious process for humans, it remains a rather difficult task for computer systems. Nevertheless, it is an important one to achieve if the goal is to realise a naturalistic communication between humans and machines. Machines that are able to sense social attitudes like agreement and disagreement and respond to them in a meaningful way are likely to be welcomed by users due to the more natural, efficient and human-centered interaction they are bound to experience. This paper surveys the nonverbal cues that could be present during agreement and disagreement behavioural displays and lists a number of tools that could be useful in detecting them, as well as a few publicly available databases that could be used to train these tools for analysis of spontaneous, audiovisual instances of agreement and disagreement.

Modeling hidden dynamics of multimodal cues for spontaneous agreement and disagreement recognition

This paper attempts to recognize spontaneous agreement and disagreement based only on nonverbal multi-modal cues. Related work has mainly used verbal and prosodic cues. We demonstrate that it is possible to correctly recognize agreement and disagreement without the use of verbal context (i.e. words, syntax). We propose to explicitly model the complex hidden dynamics of the multimodal cues using a sequential discriminative model, the Hidden Conditional Random Field (HCRF). In this paper, we show that the HCRF model is able to capture what makes each of these social attitudes unique. We present an efficient technique to analyze the concepts learned by the HCRF model and show that these coincide with the findings from social psychology regarding which cues are most prevalent in agreement and disagreement. Our experiments are performed on a spontaneous dataset of real televised debates. The HCRF model outperforms conventional approaches such as Hidden Markov Models and Support Vector Machines.

Infinite Hidden Conditional Random Fields for Human Behavior Analysis

Hidden conditional random fields (HCRFs) are discriminative latent variable models that have been shown to successfully learn the hidden structure of a given classification problem (provided an appropriate validation of the number of hidden states). In this brief, we present the infinite HCRF (iHCRF), which is a nonparametric model based on hierarchical Dirichlet processes and is capable of automatically learning the optimal number of hidden states for a classification task. We show how we learn the model hyperparameters with an effective Markov-chain Monte Carlo sampling technique, and we explain the process that underlines our iHCRF model with the Restaurant Franchise Rating Agencies analogy. We show that the iHCRF is able to converge to a correct number of represented hidden states, and outperforms the best finite HCRFs-chosen via cross-validation-for the difficult tasks of recognizing instances of agreement, disagreement, and pain. Moreover, the iHCRF manages to achieve this performance in significantly less total training, validation, and testing time.

A Deep Matrix Factorization Method for Learning Attribute Representations

Semi-Non-negative Matrix Factorization is a technique that learns a low-dimensional representation of a dataset that lends itself to a clustering interpretation. It is possible that the mapping between this new representation and our original data matrix contains rather complex hierarchical information with implicit lower-level hidden attributes, that classical one level clustering methodologies cannot interpret. In this work we propose a novel model, Deep Semi-NMF, that is able to learn such hidden representations that allow themselves to an interpretation of clustering according to different, unknown attributes of a given dataset. We also present a semi-supervised version of the algorithm, named Deep WSF, that allows the use of (partial) prior information for each of the known attributes of a dataset, that allows the model to be used on datasets with mixed attribute knowledge. Finally, we show that our models are able to learn low-dimensional representations that are better suited for clustering, but also classification, outperforming Semi-Non-negative Matrix Factorization, but also other state-of-the-art methodologies variants.

A scouting-inspired evolutionary algorithm

The goal of an evolutionary algorithm (EA) is to find the global optimum in a state space of potential solutions. But these systems can become trapped in local optima due to the EA having only generational information. Using the scouting algorithm (SA) it is suggested that a cross-generation memory mechanism can be added to modulate fitness relative to how well a region has previously been sampled. Thus, the goal is to allow the scouting-inspired EA (SEA) to leave well explore regions to find the global optimum more quickly. It will be shown that the SEA does achieve this goal for the problem domain of nonlinear programming (NLP).

Variational Infinite Hidden Conditional Random Fields

Hidden conditional random fields (HCRFs) are discriminative latent variable models which have been shown to successfully learn the hidden structure of a given classification problem. An Infinite hidden conditional random field is a hidden conditional random field with a countably infinite number of hidden states, which rids us not only of the necessity to specify a priori a fixed number of hidden states available but also of the problem of overfitting. Markov chain Monte Carlo (MCMC) sampling algorithms are often employed for inference in such models. However, convergence of such algorithms is rather difficult to verify, and as the complexity of the task at hand increases the computational cost of such algorithms often becomes prohibitive. These limitations can be overcome by variational techniques. In this paper, we present a generalized framework for infinite HCRF models, and a novel variational inference approach on a model based on coupled Dirichlet Process Mixtures, the HCRF-DPM. We show that the variational HCRF-DPM is able to converge to a correct number of represented hidden states, and performs as well as the best parametric HCRFs—chosen via cross-validation—for the difficult tasks of recognizing instances of agreement, disagreement, and pain in audiovisual sequences.

Improving Evolutionary Algorithms with Scouting: High---Dimensional Problems

Evolutionary Algorithms (EAs) are common optimization techniques based on the concept of Darwinian evolution. During the search for the global optimum of a search space, a traditional EA will often become trapped in a local optimum. The Scouting-Inspired Evolutionary Algorithms (SEAs) are a recently---introduced family of EAs that use a cross---generational memory mechanism to overcome this problem and discover solutions of higher fitness. The merit of the SEAs has been established in previous work with a number of two and three-dimensional test cases and a variety of configurations. In this paper, we will present two approaches to using SEAs to solve high---dimensional problems. The first one involves the use of Locality Sensitive Hashing (LSH) for the repository of individuals, whereas the second approach entails the use of scouting---driven mutation at a certain rate, the Scouting Rate. We will show that an SEA significantly improves the equivalent simple EA configuration with higher---dimensional problems in an expeditious manner.