742 research outputs found
Multi-task CNN Model for Attribute Prediction
This paper proposes a joint multi-task learning algorithm to better predict
attributes in images using deep convolutional neural networks (CNN). We
consider learning binary semantic attributes through a multi-task CNN model,
where each CNN will predict one binary attribute. The multi-task learning
allows CNN models to simultaneously share visual knowledge among different
attribute categories. Each CNN will generate attribute-specific feature
representations, and then we apply multi-task learning on the features to
predict their attributes. In our multi-task framework, we propose a method to
decompose the overall model's parameters into a latent task matrix and
combination matrix. Furthermore, under-sampled classifiers can leverage shared
statistics from other classifiers to improve their performance. Natural
grouping of attributes is applied such that attributes in the same group are
encouraged to share more knowledge. Meanwhile, attributes in different groups
will generally compete with each other, and consequently share less knowledge.
We show the effectiveness of our method on two popular attribute datasets.Comment: 11 pages, 3 figures, ieee transaction pape
Discriminative Adversarial Domain Adaptation
Given labeled instances on a source domain and unlabeled ones on a target
domain, unsupervised domain adaptation aims to learn a task classifier that can
well classify target instances. Recent advances rely on domain-adversarial
training of deep networks to learn domain-invariant features. However, due to
an issue of mode collapse induced by the separate design of task and domain
classifiers, these methods are limited in aligning the joint distributions of
feature and category across domains. To overcome it, we propose a novel
adversarial learning method termed Discriminative Adversarial Domain Adaptation
(DADA). Based on an integrated category and domain classifier, DADA has a novel
adversarial objective that encourages a mutually inhibitory relation between
category and domain predictions for any input instance. We show that under
practical conditions, it defines a minimax game that can promote the joint
distribution alignment. Except for the traditional closed set domain
adaptation, we also extend DADA for extremely challenging problem settings of
partial and open set domain adaptation. Experiments show the efficacy of our
proposed methods and we achieve the new state of the art for all the three
settings on benchmark datasets.Comment: 18 pages, 10 figures, 12 tables, accepted by AAAI-2
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