33 research outputs found
Adaptive Deep Learning through Visual Domain Localization
A commercial robot, trained by its manufacturer to recognize a predefined number and type of objects, might be used in many settings, that will in general differ in their illumination conditions, background, type and degree of clutter, and so on. Recent computer vision works tackle this generalization issue through domain adaptation methods, assuming as source the visual domain where the system is trained and as target the domain of deployment. All approaches assume to have access to images from all classes of the target during training, an unrealistic condition in robotics applications. We address this issue proposing an algorithm that takes into account the specific needs of robot vision. Our intuition is that the nature of the domain shift experienced mostly in robotics is local. We exploit this through the learning of maps that spatially ground the domain and quantify the degree of shift, embedded into an end-to-end deep domain adaptation architecture. By explicitly localizing the roots of the domain shift we significantly reduce the number of parameters of the architecture to tune, we gain the flexibility necessary to deal with subset of categories in the target domain at training time, and we provide a clear feedback on the rationale behind any classification decision, which can be exploited in human-robot interactions. Experiments on two different settings of the iCub World database confirm the suitability of our method for robot vision
Wasserstein Distance Guided Representation Learning for Domain Adaptation
Domain adaptation aims at generalizing a high-performance learner on a target
domain via utilizing the knowledge distilled from a source domain which has a
different but related data distribution. One solution to domain adaptation is
to learn domain invariant feature representations while the learned
representations should also be discriminative in prediction. To learn such
representations, domain adaptation frameworks usually include a domain
invariant representation learning approach to measure and reduce the domain
discrepancy, as well as a discriminator for classification. Inspired by
Wasserstein GAN, in this paper we propose a novel approach to learn domain
invariant feature representations, namely Wasserstein Distance Guided
Representation Learning (WDGRL). WDGRL utilizes a neural network, denoted by
the domain critic, to estimate empirical Wasserstein distance between the
source and target samples and optimizes the feature extractor network to
minimize the estimated Wasserstein distance in an adversarial manner. The
theoretical advantages of Wasserstein distance for domain adaptation lie in its
gradient property and promising generalization bound. Empirical studies on
common sentiment and image classification adaptation datasets demonstrate that
our proposed WDGRL outperforms the state-of-the-art domain invariant
representation learning approaches.Comment: The Thirty-Second AAAI Conference on Artificial Intelligence (AAAI
2018
A Cloud robotics architecture to foster individual child partnership in medical facilities
Robots and automation systems have become a
valuable partner in several facets of human life: from learning
and teaching, to daily working, including health monitoring
and assistance. So far, these appealing robot-based applications
are restricted to conduct repetitive, yet useful, tasks due to the
reduced individual robots’ capabilities in terms of processing
and computation. This concern prevents current robots from
facing more complex applications related to understanding hu-
man beings and perceiving their subtle feelings. Such hardware
limitations have been already found in the computer science
field. In this domain, they are currently being addressed using
a new resource exploitation model coined as cloud computing,
which is targeted at enabling massive storage and computation
using smartly connected and inexpensive commodity hardware.
The purpose of this paper is to propose a cloud-based robotics
architecture to effectively develop complex tasks related to
hospitalized children assistance. More specifically, this paper
presents a multi-agent learning system that combines machine
learning and cloud computing using low-cost robots to (1)
collect and perceive children status, (2) build a human-readable
set of rules related to the child-robot relationship, and (3)
improve the children experience during their stay in the hos-
pital. Conducted preliminary experiments proof the feasibility
of this proposal and encourage practitioners to work towards
this direction.Peer ReviewedPostprint (published version
Deep Hashing Network for Unsupervised Domain Adaptation
In recent years, deep neural networks have emerged as a dominant machine
learning tool for a wide variety of application domains. However, training a
deep neural network requires a large amount of labeled data, which is an
expensive process in terms of time, labor and human expertise. Domain
adaptation or transfer learning algorithms address this challenge by leveraging
labeled data in a different, but related source domain, to develop a model for
the target domain. Further, the explosive growth of digital data has posed a
fundamental challenge concerning its storage and retrieval. Due to its storage
and retrieval efficiency, recent years have witnessed a wide application of
hashing in a variety of computer vision applications. In this paper, we first
introduce a new dataset, Office-Home, to evaluate domain adaptation algorithms.
The dataset contains images of a variety of everyday objects from multiple
domains. We then propose a novel deep learning framework that can exploit
labeled source data and unlabeled target data to learn informative hash codes,
to accurately classify unseen target data. To the best of our knowledge, this
is the first research effort to exploit the feature learning capabilities of
deep neural networks to learn representative hash codes to address the domain
adaptation problem. Our extensive empirical studies on multiple transfer tasks
corroborate the usefulness of the framework in learning efficient hash codes
which outperform existing competitive baselines for unsupervised domain
adaptation.Comment: CVPR 201