3 research outputs found
Multi-component Image Translation for Deep Domain Generalization
Domain adaption (DA) and domain generalization (DG) are two closely related
methods which are both concerned with the task of assigning labels to an
unlabeled data set. The only dissimilarity between these approaches is that DA
can access the target data during the training phase, while the target data is
totally unseen during the training phase in DG. The task of DG is challenging
as we have no earlier knowledge of the target samples. If DA methods are
applied directly to DG by a simple exclusion of the target data from training,
poor performance will result for a given task. In this paper, we tackle the
domain generalization challenge in two ways. In our first approach, we propose
a novel deep domain generalization architecture utilizing synthetic data
generated by a Generative Adversarial Network (GAN). The discrepancy between
the generated images and synthetic images is minimized using existing domain
discrepancy metrics such as maximum mean discrepancy or correlation alignment.
In our second approach, we introduce a protocol for applying DA methods to a DG
scenario by excluding the target data from the training phase, splitting the
source data to training and validation parts, and treating the validation data
as target data for DA. We conduct extensive experiments on four cross-domain
benchmark datasets. Experimental results signify our proposed model outperforms
the current state-of-the-art methods for DG.Comment: Accepted in WACV 201
A Concept for Deployment and Evaluation of Unsupervised Domain Adaptation in Cognitive Perception Systems
Jüngste Entwicklungen im Bereich des tiefen Lernens ermöglichen Perzeptionssystemen
datengetrieben Wissen über einen vordefinierten Betriebsbereich,
eine sogenannte Domäne, zu gewinnen. Diese Verfahren des überwachten
Lernens werden durch das Aufkommen groß angelegter annotierter
Datensätze und immer leistungsfähigerer Prozessoren vorangetrieben und
zeigen unübertroffene Performanz bei Perzeptionsaufgaben in einer Vielzahl
von Anwendungsbereichen.Jedoch sind überwacht-trainierte neuronale Netze
durch die Menge an verfügbaren annotierten Daten limitiert und dies wiederum
findet in einem begrenzten Betriebsbereich Ausdruck. Dabei beruht
überwachtes Lernen stark auf manuell durchzuführender Datenannotation.
Insbesondere durch die ständig steigende Verfügbarkeit von nicht annotierten
großen Datenmengen ist der Gebrauch von unüberwachter Domänenanpassung
entscheidend. Verfahren zur unüberwachten Domänenanpassung sind
meist nicht geeignet, um eine notwendige Inbetriebnahme des neuronalen
Netzes in einer zusätzlichen Domäne zu gewährleisten. Darüber hinaus
sind vorhandene Metriken häufig unzureichend für eine auf die Anwendung
der domänenangepassten neuronalen Netzen ausgerichtete Validierung. Der
Hauptbeitrag der vorliegenden Dissertation besteht aus neuen Konzepten zur
unüberwachten Domänenanpassung. Basierend auf einer Kategorisierung
von Domänenübergängen und a priori verfügbaren Wissensrepräsentationen
durch ein überwacht-trainiertes neuronales Netz wird eine unüberwachte
Domänenanpassung auf nicht annotierten Daten ermöglicht. Um die kontinuierliche
Bereitstellung von neuronalen Netzen für die Anwendung in
der Perzeption zu adressieren, wurden neuartige Verfahren speziell für die
unüberwachte Erweiterung des Betriebsbereichs eines neuronalen Netzes
entwickelt. Beispielhafte Anwendungsfälle des Fahrzeugsehens zeigen, wie
die neuartigen Verfahren kombiniert mit neu entwickelten Metriken zur kontinuierlichen
Inbetriebnahme von neuronalen Netzen auf nicht annotierten
Daten beitragen. Außerdem werden die Implementierungen aller entwickelten
Verfahren und Algorithmen dargestellt und öffentlich zugänglich gemacht.
Insbesondere wurden die neuartigen Verfahren erfolgreich auf die unüberwachte
Domänenanpassung, ausgehend von der Tag- auf die Nachtobjekterkennung
im Bereich des Fahrzeugsehens angewendet
Practical Robust Learning Under Domain Shifts
With the constantly upgraded devices, the data we capture is shifting with time. Despite the domain shifts among the images, we as humans can put aside the difference and still recognize the content. However, these shifts are a bigger challenge for machines. It is widely known that humans are naturally adaptive to the visual changes in the environment, without learning all over again. However, to make machines work in the changed environment we need new annotations from human. The fundamental question is: can we make machines as adaptive as humans?
In this thesis, we have worked towards addressing this question through advances in the study of robust learning under domain shifts via domain adaptation. Our goal is to facilitate the transfer of information of the machines while minimizing the need for human supervision.
To enable real systems with demonstrated robustness, the study of domain adaptation needs to move from ideals to realities. In current domain adaptation research, there are few ideals that are not consistent with reality: i) The assumption that domains are perfectly sliced and that domain labels are available. ii) The assumption that the annotations from the target domain should be treated equally as those of the source domain. iii) The assumption that the samples of target domains are constantly accessible. In this thesis, we try to address the issue that true domain labels are hard to obtain, the target domain labels have better ways to exploited, and that in reality the target domain is often time-sensitive.
In the scope of problem settings, this thesis has covered the following scenarios with practical values. Unsupervised multi-source domain adaptation, semi-supervised domain adaptation and online domain adaptation. Three completed works are reviewed corresponding to each problem setting. The first work proposes an adversarial learning strategy that learns a dynamic curriculum for source samples to maximize the utility of source labels of multiple domains. The model iteratively learns which domains or samples are best suited for aligning to the target. The intuition is to force the adversarial agent to constantly re-measure the transferability of latent domains over time to adversarially raise the error rate of the domain discriminator. The method has removed the need of domain labels, yet it outperforms other methods on four well-known benchmarks by significant margins. The second work aims to address the problem that current methods have not effectively used the target supervision by treating source and target supervision without distinction. The work points out that the labeled target data needs to be distinguished from the source, and propose to explicitly decompose the task into two sub-tasks: a semi-supervised learning task in the target domain and an unsupervised domain adaptation task across domains. By doing so, the two sub-tasks can better leverage the corresponding supervision and thus yield very different classifiers. The third work is proposed in the context of online privacy, i.e. each online sample of the target domain is permanently deleted after processed. The proposed framework utilizes the labels from the public data and predicts on the unlabeled sensitive private data. To tackle the inevitable distribution shift from the public data to the private data, the work proposes a novel domain adaptation algorithm that directly aims at the fundamental challenge of this online setting--the lack of diverse source-target data pairs