697 research outputs found
Active Learning to Classify Macromolecular Structures in situ for Less Supervision in Cryo-Electron Tomography
Motivation: Cryo-Electron Tomography (cryo-ET) is a 3D bioimaging tool that
visualizes the structural and spatial organization of macromolecules at a
near-native state in single cells, which has broad applications in life
science. However, the systematic structural recognition and recovery of
macromolecules captured by cryo-ET are difficult due to high structural
complexity and imaging limits. Deep learning based subtomogram classification
have played critical roles for such tasks. As supervised approaches, however,
their performance relies on sufficient and laborious annotation on a large
training dataset.
Results: To alleviate this major labeling burden, we proposed a Hybrid Active
Learning (HAL) framework for querying subtomograms for labelling from a large
unlabeled subtomogram pool. Firstly, HAL adopts uncertainty sampling to select
the subtomograms that have the most uncertain predictions. Moreover, to
mitigate the sampling bias caused by such strategy, a discriminator is
introduced to judge if a certain subtomogram is labeled or unlabeled and
subsequently the model queries the subtomogram that have higher probabilities
to be unlabeled. Additionally, HAL introduces a subset sampling strategy to
improve the diversity of the query set, so that the information overlap is
decreased between the queried batches and the algorithmic efficiency is
improved. Our experiments on subtomogram classification tasks using both
simulated and real data demonstrate that we can achieve comparable testing
performance (on average only 3% accuracy drop) by using less than 30% of the
labeled subtomograms, which shows a very promising result for subtomogram
classification task with limited labeling resources.Comment: Statement on authorship changes: Dr. Eric Xing was an academic
advisor of Mr. Haohan Wang. Dr. Xing was not directly involved in this work
and has no direct interaction or collaboration with any other authors on this
work. Therefore, Dr. Xing is removed from the author list according to his
request. Mr. Zhenxi Zhu's affiliation is updated to his current affiliatio
Highly Efficient Regression for Scalable Person Re-Identification
Existing person re-identification models are poor for scaling up to large
data required in real-world applications due to: (1) Complexity: They employ
complex models for optimal performance resulting in high computational cost for
training at a large scale; (2) Inadaptability: Once trained, they are
unsuitable for incremental update to incorporate any new data available. This
work proposes a truly scalable solution to re-id by addressing both problems.
Specifically, a Highly Efficient Regression (HER) model is formulated by
embedding the Fisher's criterion to a ridge regression model for very fast
re-id model learning with scalable memory/storage usage. Importantly, this new
HER model supports faster than real-time incremental model updates therefore
making real-time active learning feasible in re-id with human-in-the-loop.
Extensive experiments show that such a simple and fast model not only
outperforms notably the state-of-the-art re-id methods, but also is more
scalable to large data with additional benefits to active learning for reducing
human labelling effort in re-id deployment
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