3,609 research outputs found
Information-Theoretic Active Learning for Content-Based Image Retrieval
We propose Information-Theoretic Active Learning (ITAL), a novel batch-mode
active learning method for binary classification, and apply it for acquiring
meaningful user feedback in the context of content-based image retrieval.
Instead of combining different heuristics such as uncertainty, diversity, or
density, our method is based on maximizing the mutual information between the
predicted relevance of the images and the expected user feedback regarding the
selected batch. We propose suitable approximations to this computationally
demanding problem and also integrate an explicit model of user behavior that
accounts for possible incorrect labels and unnameable instances. Furthermore,
our approach does not only take the structure of the data but also the expected
model output change caused by the user feedback into account. In contrast to
other methods, ITAL turns out to be highly flexible and provides
state-of-the-art performance across various datasets, such as MIRFLICKR and
ImageNet.Comment: GCPR 2018 paper (14 pages text + 2 pages references + 6 pages
appendix
Label-efficient Time Series Representation Learning: A Review
The scarcity of labeled data is one of the main challenges of applying deep
learning models on time series data in the real world. Therefore, several
approaches, e.g., transfer learning, self-supervised learning, and
semi-supervised learning, have been recently developed to promote the learning
capability of deep learning models from the limited time series labels. In this
survey, for the first time, we provide a novel taxonomy to categorize existing
approaches that address the scarcity of labeled data problem in time series
data based on their dependency on external data sources. Moreover, we present a
review of the recent advances in each approach and conclude the limitations of
the current works and provide future directions that could yield better
progress in the field.Comment: Under Revie
ALBADross: active learning based anomaly diagnosis for production HPC systems
000000000000000000000000000000000000000000000000000002263712 - Sandia National Laboratories; Sandia National LaboratoriesAccepted manuscrip
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