3,956 research outputs found
SEVEN: Deep Semi-supervised Verification Networks
Verification determines whether two samples belong to the same class or not,
and has important applications such as face and fingerprint verification, where
thousands or millions of categories are present but each category has scarce
labeled examples, presenting two major challenges for existing deep learning
models. We propose a deep semi-supervised model named SEmi-supervised
VErification Network (SEVEN) to address these challenges. The model consists of
two complementary components. The generative component addresses the lack of
supervision within each category by learning general salient structures from a
large amount of data across categories. The discriminative component exploits
the learned general features to mitigate the lack of supervision within
categories, and also directs the generative component to find more informative
structures of the whole data manifold. The two components are tied together in
SEVEN to allow an end-to-end training of the two components. Extensive
experiments on four verification tasks demonstrate that SEVEN significantly
outperforms other state-of-the-art deep semi-supervised techniques when labeled
data are in short supply. Furthermore, SEVEN is competitive with fully
supervised baselines trained with a larger amount of labeled data. It indicates
the importance of the generative component in SEVEN.Comment: 7 pages, 2 figures, accepted to the 2017 International Joint
Conference on Artificial Intelligence (IJCAI-17
Structured Domain Adaptation with Online Relation Regularization for Unsupervised Person Re-ID
Unsupervised domain adaptation (UDA) aims at adapting the model trained on a
labeled source-domain dataset to an unlabeled target-domain dataset. The task
of UDA on open-set person re-identification (re-ID) is even more challenging as
the identities (classes) do not overlap between the two domains. One major
research direction was based on domain translation, which, however, has fallen
out of favor in recent years due to inferior performance compared to
pseudo-label-based methods. We argue that translation-based methods have great
potential on exploiting the valuable source-domain data but they did not
provide proper regularization on the translation process. Specifically, these
methods only focus on maintaining the identities of the translated images while
ignoring the inter-sample relation during translation. To tackle the challenge,
we propose an end-to-end structured domain adaptation framework with an online
relation-consistency regularization term. During training, the person feature
encoder is optimized to model inter-sample relations on-the-fly for supervising
relation-consistency domain translation, which in turn, improves the encoder
with informative translated images. An improved pseudo-label-based encoder can
therefore be obtained by jointly training the source-to-target translated
images with ground-truth identities and target-domain images with pseudo
identities. In the experiments, our proposed framework is shown to outperform
state-of-the-art methods on multiple UDA tasks of person re-ID. Code is
available at https://github.com/yxgeee/SDA
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