1,828 research outputs found
Efficient and Parsimonious Agnostic Active Learning
Abstract We develop a new active learning algorithm for the streaming setting satisfying three important properties: 1) It provably works for any classifier representation and classification problem including those with severe noise. 2) It is efficiently implementable with an ERM oracle. 3) It is more aggressive than all previous approaches satisfying 1 and 2. To do this, we create an algorithm based on a newly defined optimization problem and analyze it. We also conduct the first experimental analysis of all efficient agnostic active learning algorithms, evaluating their strengths and weaknesses in different settings
Selectively inhibiting learning bias for active sampling
Efficient training of machine learning algorithms requires a reliable labeled set from the application domain. Usually, data labeling is a costly process. Therefore, a selective approach is desirable. Active learning has been successfully used to reduce the labeling effort, due to its parsimonious process of querying the labeler. Nevertheless, many active learning strategies are dependent on early predictions made by learning algorithms. This might be a major problem when the learner is still unable to provide reliable information. In this context, agnostic strategies can be convenient, since they spare internal learners - usually favoring exploratory queries. On the other hand, prospective queries could benefit from a learning bias. In this article, we highlight the advantages of the agnostic approach and propose how to explore some of them without foregoing prospection. A simple hybrid strategy and a visualization tool called ranking curves, are proposed as a proof of concept. The tool allowed to see clearly when the presence of a learner was possibly detrimental. Finally, the hybrid strategy was successfully compared to its counterpart in the literature, to pure agnostic strategies and to the usual baseline of the field.CAPESCNPqFAPES
Coplanar Repeats by Energy Minimization
This paper proposes an automated method to detect, group and rectify
arbitrarily-arranged coplanar repeated elements via energy minimization. The
proposed energy functional combines several features that model how planes with
coplanar repeats are projected into images and captures global interactions
between different coplanar repeat groups and scene planes. An inference
framework based on a recent variant of -expansion is described and fast
convergence is demonstrated. We compare the proposed method to two widely-used
geometric multi-model fitting methods using a new dataset of annotated images
containing multiple scene planes with coplanar repeats in varied arrangements.
The evaluation shows a significant improvement in the accuracy of
rectifications computed from coplanar repeats detected with the proposed method
versus those detected with the baseline methods.Comment: 14 pages with supplemental materials attache
The Incremental Multiresolution Matrix Factorization Algorithm
Multiresolution analysis and matrix factorization are foundational tools in
computer vision. In this work, we study the interface between these two
distinct topics and obtain techniques to uncover hierarchical block structure
in symmetric matrices -- an important aspect in the success of many vision
problems. Our new algorithm, the incremental multiresolution matrix
factorization, uncovers such structure one feature at a time, and hence scales
well to large matrices. We describe how this multiscale analysis goes much
farther than what a direct global factorization of the data can identify. We
evaluate the efficacy of the resulting factorizations for relative leveraging
within regression tasks using medical imaging data. We also use the
factorization on representations learned by popular deep networks, providing
evidence of their ability to infer semantic relationships even when they are
not explicitly trained to do so. We show that this algorithm can be used as an
exploratory tool to improve the network architecture, and within numerous other
settings in vision.Comment: Computer Vision and Pattern Recognition (CVPR) 2017, 10 page
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