2 research outputs found
Generalized Zero-Shot Learning via Synthesized Examples
We present a generative framework for generalized zero-shot learning where
the training and test classes are not necessarily disjoint. Built upon a
variational autoencoder based architecture, consisting of a probabilistic
encoder and a probabilistic conditional decoder, our model can generate novel
exemplars from seen/unseen classes, given their respective class attributes.
These exemplars can subsequently be used to train any off-the-shelf
classification model. One of the key aspects of our encoder-decoder
architecture is a feedback-driven mechanism in which a discriminator (a
multivariate regressor) learns to map the generated exemplars to the
corresponding class attribute vectors, leading to an improved generator. Our
model's ability to generate and leverage examples from unseen classes to train
the classification model naturally helps to mitigate the bias towards
predicting seen classes in generalized zero-shot learning settings. Through a
comprehensive set of experiments, we show that our model outperforms several
state-of-the-art methods, on several benchmark datasets, for both standard as
well as generalized zero-shot learning.Comment: Accepted in CVPR'1
Leveraging Uncertainty Estimates To Improve Classifier Performance
Binary classification involves predicting the label of an instance based on
whether the model score for the positive class exceeds a threshold chosen based
on the application requirements (e.g., maximizing recall for a precision
bound). However, model scores are often not aligned with the true positivity
rate. This is especially true when the training involves a differential
sampling across classes or there is distributional drift between train and test
settings. In this paper, we provide theoretical analysis and empirical evidence
of the dependence of model score estimation bias on both uncertainty and score
itself. Further, we formulate the decision boundary selection in terms of both
model score and uncertainty, prove that it is NP-hard, and present algorithms
based on dynamic programming and isotonic regression. Evaluation of the
proposed algorithms on three real-world datasets yield 25%-40% gain in recall
at high precision bounds over the traditional approach of using model score
alone, highlighting the benefits of leveraging uncertainty