Semi-Supervised Class Discovery
- URL: http://arxiv.org/abs/2002.03480v2
- Date: Sat, 22 Feb 2020 01:31:08 GMT
- Title: Semi-Supervised Class Discovery
- Authors: Jeremy Nixon, Jeremiah Liu, David Berthelot
- Abstract summary: We introduce the dataset Reconstruction Accuracy, a new and important measure of the effectiveness of a model's ability to create labels.
We apply a new, class learnability, for deciding whether a class is worthy of addition to the training dataset.
We show that our class discovery system can be successfully applied to vision and language.
- Score: 7.123519086758813
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: One promising approach to dealing with datapoints that are outside of the
initial training distribution (OOD) is to create new classes that capture
similarities in the datapoints previously rejected as uncategorizable. Systems
that generate labels can be deployed against an arbitrary amount of data,
discovering classification schemes that through training create a higher
quality representation of data. We introduce the Dataset Reconstruction
Accuracy, a new and important measure of the effectiveness of a model's ability
to create labels. We introduce benchmarks against this Dataset Reconstruction
metric. We apply a new heuristic, class learnability, for deciding whether a
class is worthy of addition to the training dataset. We show that our class
discovery system can be successfully applied to vision and language, and we
demonstrate the value of semi-supervised learning in automatically discovering
novel classes.
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