Improved Techniques for Adversarial Discriminative Domain Adaptation

Adversarial discriminative domain adaptation (ADDA) is an efficient framework for unsupervised domain adaptation in image classification, where the source and target domains are assumed to have the same classes, but no labels are available for the target domain. We investigate whether we can improve performance of ADDA with a new framework and new loss formulations. Following the framework of semi-supervised GANs, we first extend the discriminator output over the source classes, in order to model the joint distribution over domain and task. We thus leverage on the distribution over the source encoder posteriors (which is fixed during adversarial training) and propose maximum mean discrepancy (MMD) and reconstruction-based loss functions for aligning the target encoder distribution to the source domain. We compare and provide a comprehensive analysis of how our framework and loss formulations extend over simple multi-class extensions of ADDA and other discriminative variants of semi-supervised GANs. In addition, we introduce various forms of regularization for stabilizing training, including treating the discriminator as a denoising autoencoder and regularizing the target encoder with source examples to reduce overfitting under a contraction mapping (i.e., when the target per-class distributions are contracting during alignment with the source). Finally, we validate our framework on standard domain adaptation datasets, such as SVHN and MNIST. We also examine how our framework benefits recognition problems based on modalities that lack training data, by introducing and evaluating on a neuromorphic vision sensing (NVS) sign language recognition dataset, where the source and target domains constitute emulated and real neuromorphic spike events respectively. Our results on all datasets show that our proposal competes or outperforms the state-of-the-art in unsupervised domain adaptation.Comment: To appear in IEEE Transactions on Image Processin

Combining vocal tract length normalization with hierarchial linear transformations

Recent research has demonstrated the effectiveness of vocal tract length normalization (VTLN) as a rapid adaptation technique for statistical parametric speech synthesis. VTLN produces speech with naturalness preferable to that of MLLR-based adaptation techniques, being much closer in quality to that generated by the original av-erage voice model. However with only a single parameter, VTLN captures very few speaker specific characteristics when compared to linear transform based adaptation techniques. This paper pro-poses that the merits of VTLN can be combined with those of linear transform based adaptation in a hierarchial Bayesian frame-work, where VTLN is used as the prior information. A novel tech-nique for propagating the gender information from the VTLN prior through constrained structural maximum a posteriori linear regres-sion (CSMAPLR) adaptation is presented. Experiments show that the resulting transformation has improved speech quality with better naturalness, intelligibility and improved speaker similarity. Index Terms — Statistical parametric speech synthesis, hidden Markov models, speaker adaptation, vocal tract length normaliza-tion, constrained structural maximum a posteriori linear regression 1

Self-adaptation of Genetic Operators Through Genetic Programming Techniques

Here we propose an evolutionary algorithm that self modifies its operators at the same time that candidate solutions are evolved. This tackles convergence and lack of diversity issues, leading to better solutions. Operators are represented as trees and are evolved using genetic programming (GP) techniques. The proposed approach is tested with real benchmark functions and an analysis of operator evolution is provided.Comment: Presented in GECCO 201

Callback adaptation techniques

This document describes a callback adaptation technique developed for the PortAudio port on ASIO. This method handle buffers of different sizes and guarantee lowest latency added by buffer size adaptation

The effect of climate change adaptation strategies on bean yield in central and northern Uganda

This paper analyses the impact of adaptation to climate change on bean productivity on a micro-scale using instrumental variable techniques in a two-stage econometric model, using data collected from farming households in northern and central Uganda. We employed the bivariate probit technique to model simultaneous and interdependent adoption decisions, and the ordered probit to model the intensity of adaptation. We modelled the impact of adaptation using instrumental variables and the control function approach because of the potential endogeneity of the adaptation decision. The driving forces behind adoption of the two selected adaptation strategies were heterogeneous. Location-specific factors influenced the intensity of adaptation between the two study regions. The effect of adaptation was stronger for households that used a higher number of strategies, evidence that the two adaptation strategies need to be used simultaneously by farmers to maximise the positive impact of adaptation

Visual adaptation to goal-directed hand actions

Prolonged exposure to visual stimuli, or adaptation, often results in an adaptation “aftereffect” which can profoundly distort our perception of subsequent visual stimuli. This technique has been commonly used to investigate mechanisms underlying our perception of simple visual stimuli, and more recently, of static faces. We tested whether humans would adapt to movies of hands grasping and placing different weight objects. After adapting to hands grasping light or heavy objects, subsequently perceived objects appeared relatively heavier, or lighter, respectively. The aftereffects increased logarithmically with adaptation action repetition and decayed logarithmically with time. Adaptation aftereffects also indicated that perception of actions relies predominantly on view-dependent mechanisms. Adapting to one action significantly influenced the perception of the opposite action. These aftereffects can only be explained by adaptation of mechanisms that take into account the presence/absence of the object in the hand. We tested if evidence on action processing mechanisms obtained using visual adaptation techniques confirms underlying neural processing. We recorded monkey superior temporal sulcus (STS) single-cell responses to hand actions. Cells sensitive to grasping or placing typically responded well to the opposite action; cells also responded during different phases of the actions. Cell responses were sensitive to the view of the action and were dependent upon the presence of the object in the scene. We show here that action processing mechanisms established using visual adaptation parallel the neural mechanisms revealed during recording from monkey STS. Visual adaptation techniques can thus be usefully employed to investigate brain mechanisms underlying action perception.Publisher PDFPeer reviewe

Modeling Adaptation with Klaim

In recent years, it has been argued that systems and applications, in order to deal with their increasing complexity, should be able to adapt their behavior according to new requirements or environment conditions. In this paper, we present an investigation aiming at studying how coordination languages and formal methods can contribute to a better understanding, implementation and use of the mechanisms and techniques for adaptation currently proposed in the literature. Our study relies on the formal coordination language Klaim as a common framework for modeling some well-known adaptation techniques: the IBM MAPE-K loop, the Accord component-based framework for architectural adaptation, and the aspect- and context-oriented programming paradigms. We illustrate our approach through a simple example concerning a data repository equipped with an automated cache mechanism

Agree to Disagree: Security Requirements Are Different, But Mechanisms For Security Adaptation Are Not

We describe a dialogue between a proponent and an opponent of the proposition "security is not just another quality attribute in self-adaptive systems". The dialogue is structured in two steps. First, we examine whether security requirements are different from other system-level requirements. Our consensus is that security requirements require specific methods for elicitation, reasoning, and analysis. However, other requirements (such as safety, usability and performance) also require specific techniques. Then, we examine the adaptation mechanisms for security and compare them with other properties. Our consensus is that most adaptation techniques can be applied to maintain security and other requirements alike

Modeling adaptation with a tuple-based coordination language

In recent years, it has been argued that systems and applications, in order to deal with their increasing complexity, should be able to adapt their behavior according to new requirements or environment conditions. In this paper, we present a preliminary investigation aiming at studying how coordination languages and formal methods can contribute to a better understanding, implementation and usage of the mechanisms and techniques for adaptation currently proposed in the literature. Our study relies on the formal coordination language Klaim as a common framework for modeling some adaptation techniques, namely the MAPE-K loop, aspect- and context-oriented programming