12,122 research outputs found
Domain Generalization by Solving Jigsaw Puzzles
Human adaptability relies crucially on the ability to learn and merge
knowledge both from supervised and unsupervised learning: the parents point out
few important concepts, but then the children fill in the gaps on their own.
This is particularly effective, because supervised learning can never be
exhaustive and thus learning autonomously allows to discover invariances and
regularities that help to generalize. In this paper we propose to apply a
similar approach to the task of object recognition across domains: our model
learns the semantic labels in a supervised fashion, and broadens its
understanding of the data by learning from self-supervised signals how to solve
a jigsaw puzzle on the same images. This secondary task helps the network to
learn the concepts of spatial correlation while acting as a regularizer for the
classification task. Multiple experiments on the PACS, VLCS, Office-Home and
digits datasets confirm our intuition and show that this simple method
outperforms previous domain generalization and adaptation solutions. An
ablation study further illustrates the inner workings of our approach.Comment: Accepted at CVPR 2019 (oral
Information Extraction in Illicit Domains
Extracting useful entities and attribute values from illicit domains such as
human trafficking is a challenging problem with the potential for widespread
social impact. Such domains employ atypical language models, have `long tails'
and suffer from the problem of concept drift. In this paper, we propose a
lightweight, feature-agnostic Information Extraction (IE) paradigm specifically
designed for such domains. Our approach uses raw, unlabeled text from an
initial corpus, and a few (12-120) seed annotations per domain-specific
attribute, to learn robust IE models for unobserved pages and websites.
Empirically, we demonstrate that our approach can outperform feature-centric
Conditional Random Field baselines by over 18\% F-Measure on five annotated
sets of real-world human trafficking datasets in both low-supervision and
high-supervision settings. We also show that our approach is demonstrably
robust to concept drift, and can be efficiently bootstrapped even in a serial
computing environment.Comment: 10 pages, ACM WWW 201
A foundation for machine learning in design
This paper presents a formalism for considering the issues of learning in design. A foundation for machine learning in design (MLinD) is defined so as to provide answers to basic questions on learning in design, such as, "What types of knowledge can be learnt?", "How does learning occur?", and "When does learning occur?". Five main elements of MLinD are presented as the input knowledge, knowledge transformers, output knowledge, goals/reasons for learning, and learning triggers. Using this foundation, published systems in MLinD were reviewed. The systematic review presents a basis for validating the presented foundation. The paper concludes that there is considerable work to be carried out in order to fully formalize the foundation of MLinD
Generating target system specifications from a domain model using CLIPS
The quest for reuse in software engineering is still being pursued and researchers are actively investigating the domain modeling approach to software construction. There are several domain modeling efforts reported in the literature and they all agree that the components that are generated from domain modeling are more conducive to reuse. Once a domain model is created, several target systems can be generated by tailoring the domain model or by evolving the domain model and then tailoring it according to the specified requirements. This paper presents the Evolutionary Domain Life Cycle (EDLC) paradigm in which a domain model is created using multiple views, namely, aggregation hierarchy, generalization/specialization hierarchies, object communication diagrams and state transition diagrams. The architecture of the Knowledge Based Requirements Elicitation Tool (KBRET) which is used to generate target system specifications is also presented. The preliminary version of KBRET is implemented in the C Language Integrated Production System (CLIPS)
Episodic Training for Domain Generalization
Domain generalization (DG) is the challenging and topical problem of learning
models that generalize to novel testing domains with different statistics than
a set of known training domains. The simple approach of aggregating data from
all source domains and training a single deep neural network end-to-end on all
the data provides a surprisingly strong baseline that surpasses many prior
published methods. In this paper, we build on this strong baseline by designing
an episodic training procedure that trains a single deep network in a way that
exposes it to the domain shift that characterises a novel domain at runtime.
Specifically, we decompose a deep network into feature extractor and classifier
components, and then train each component by simulating it interacting with a
partner who is badly tuned for the current domain. This makes both components
more robust, ultimately leading to our networks producing state-of-the-art
performance on three DG benchmarks. Furthermore, we consider the pervasive
workflow of using an ImageNet trained CNN as a fixed feature extractor for
downstream recognition tasks. Using the Visual Decathlon benchmark, we
demonstrate that our episodic-DG training improves the performance of such a
general-purpose feature extractor by explicitly training a feature for
robustness to novel problems. This shows that DG training can benefit standard
practice in computer vision.Comment: ICCV'19 CR version and fix Table 5. Code is now available at
https://github.com/HAHA-DL/Episodic-D
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