50,115 research outputs found
Picking groups instead of samples: a close look at Static Pool-based Meta-Active Learning
©2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Active Learning techniques are used to tackle learning problems where obtaining training labels is costly. In this work we use Meta-Active Learning to learn to select a subset of samples from a pool of unsupervised input for further annotation. This scenario is called Static Pool-based Meta-Active Learning. We propose to extend existing approaches by performing the selection in a manner that, unlike previous works, can handle the selection of each sample based on the whole selected subset.Peer ReviewedPostprint (author's final draft
Committee-Based Sample Selection for Probabilistic Classifiers
In many real-world learning tasks, it is expensive to acquire a sufficient
number of labeled examples for training. This paper investigates methods for
reducing annotation cost by `sample selection'. In this approach, during
training the learning program examines many unlabeled examples and selects for
labeling only those that are most informative at each stage. This avoids
redundantly labeling examples that contribute little new information. Our work
follows on previous research on Query By Committee, extending the
committee-based paradigm to the context of probabilistic classification. We
describe a family of empirical methods for committee-based sample selection in
probabilistic classification models, which evaluate the informativeness of an
example by measuring the degree of disagreement between several model variants.
These variants (the committee) are drawn randomly from a probability
distribution conditioned by the training set labeled so far. The method was
applied to the real-world natural language processing task of stochastic
part-of-speech tagging. We find that all variants of the method achieve a
significant reduction in annotation cost, although their computational
efficiency differs. In particular, the simplest variant, a two member committee
with no parameters to tune, gives excellent results. We also show that sample
selection yields a significant reduction in the size of the model used by the
tagger
Suggestive Annotation: A Deep Active Learning Framework for Biomedical Image Segmentation
Image segmentation is a fundamental problem in biomedical image analysis.
Recent advances in deep learning have achieved promising results on many
biomedical image segmentation benchmarks. However, due to large variations in
biomedical images (different modalities, image settings, objects, noise, etc),
to utilize deep learning on a new application, it usually needs a new set of
training data. This can incur a great deal of annotation effort and cost,
because only biomedical experts can annotate effectively, and often there are
too many instances in images (e.g., cells) to annotate. In this paper, we aim
to address the following question: With limited effort (e.g., time) for
annotation, what instances should be annotated in order to attain the best
performance? We present a deep active learning framework that combines fully
convolutional network (FCN) and active learning to significantly reduce
annotation effort by making judicious suggestions on the most effective
annotation areas. We utilize uncertainty and similarity information provided by
FCN and formulate a generalized version of the maximum set cover problem to
determine the most representative and uncertain areas for annotation. Extensive
experiments using the 2015 MICCAI Gland Challenge dataset and a lymph node
ultrasound image segmentation dataset show that, using annotation suggestions
by our method, state-of-the-art segmentation performance can be achieved by
using only 50% of training data.Comment: Accepted at MICCAI 201
- …