Deep Active Learning Explored Across Diverse Label Spaces

Deep learning architectures have been widely explored in computer vision and have

depicted commendable performance in a variety of applications. A fundamental challenge

in training deep networks is the requirement of large amounts of labeled training

data. While gathering large quantities of unlabeled data is cheap and easy, annotating

the data is an expensive process in terms of time, labor and human expertise.

Thus, developing algorithms that minimize the human effort in training deep models

is of immense practical importance. Active learning algorithms automatically identify

salient and exemplar samples from large amounts of unlabeled data and can augment

maximal information to supervised learning models, thereby reducing the human annotation

effort in training machine learning models. The goal of this dissertation is to

fuse ideas from deep learning and active learning and design novel deep active learning

algorithms. The proposed learning methodologies explore diverse label spaces to

solve different computer vision applications. Three major contributions have emerged

from this work; (i) a deep active framework for multi-class image classication, (ii)

a deep active model with and without label correlation for multi-label image classi-

cation and (iii) a deep active paradigm for regression. Extensive empirical studies

on a variety of multi-class, multi-label and regression vision datasets corroborate the

potential of the proposed methods for real-world applications. Additional contributions

include: (i) a multimodal emotion database consisting of recordings of facial

expressions, body gestures, vocal expressions and physiological signals of actors enacting

various emotions, (ii) four multimodal deep belief network models and (iii)

an in-depth analysis of the effect of transfer of multimodal emotion features between

source and target networks on classification accuracy and training time. These related

contributions help comprehend the challenges involved in training deep learning

models and motivate the main goal of this dissertation.