20 research outputs found
Human Active Learning
Active machine learning (AML) is a popular research area in machine learning. It allows selection of the most informative instances in training data of the domain for manual labeling. AML aims to produce a highly accurate classifier using as few labeled instances as possible, thereby minimizing the cost of obtaining labeled data. As machines can learn from experience like humans do, using AML for human category learning may help human learning become more efficient and hence reduce the cost of teaching. This chapter is a review of recent research literature concerning the use of AML technique to enhance human learning and teaching. There are a few studies on the applications of AML to the human category learning domain. The most interesting study was by Castro et al., which showed that humans learn faster with better performance when they can actively select the informative instances from a pool of unlabeled data instead of random sampling. Although AML can facilitate object categorization for humans, there are still many challenges and questions that need to be addressed in the use of AML for modeling human categorization. In this chapter, we will discuss some of these challenges
DAugNet: Unsupervised, Multi-source, Multi-target, and Life-long Domain Adaptation for Semantic Segmentation of Satellite Images
The domain adaptation of satellite images has recently gained an increasing
attention to overcome the limited generalization abilities of machine learning
models when segmenting large-scale satellite images. Most of the existing
approaches seek for adapting the model from one domain to another. However,
such single-source and single-target setting prevents the methods from being
scalable solutions, since nowadays multiple source and target domains having
different data distributions are usually available. Besides, the continuous
proliferation of satellite images necessitates the classifiers to adapt to
continuously increasing data. We propose a novel approach, coined DAugNet, for
unsupervised, multi-source, multi-target, and life-long domain adaptation of
satellite images. It consists of a classifier and a data augmentor. The data
augmentor, which is a shallow network, is able to perform style transfer
between multiple satellite images in an unsupervised manner, even when new data
are added over the time. In each training iteration, it provides the classifier
with diversified data, which makes the classifier robust to large data
distribution difference between the domains. Our extensive experiments prove
that DAugNet significantly better generalizes to new geographic locations than
the existing approaches
MLAN: Multi-Level Adversarial Network for Domain Adaptive Semantic Segmentation
Recent progresses in domain adaptive semantic segmentation demonstrate the
effectiveness of adversarial learning (AL) in unsupervised domain adaptation.
However, most adversarial learning based methods align source and target
distributions at a global image level but neglect the inconsistency around
local image regions. This paper presents a novel multi-level adversarial
network (MLAN) that aims to address inter-domain inconsistency at both global
image level and local region level optimally. MLAN has two novel designs,
namely, region-level adversarial learning (RL-AL) and co-regularized
adversarial learning (CR-AL). Specifically, RL-AL models prototypical regional
context-relations explicitly in the feature space of a labelled source domain
and transfers them to an unlabelled target domain via adversarial learning.
CR-AL fuses region-level AL and image-level AL optimally via mutual
regularization. In addition, we design a multi-level consistency map that can
guide domain adaptation in both input space (, image-to-image
translation) and output space (, self-training) effectively. Extensive
experiments show that MLAN outperforms the state-of-the-art with a large margin
consistently across multiple datasets.Comment: Submitted to P
Multi-Domain Active Learning: A Comparative Study
Building classifiers on multiple domains is a practical problem in the real
life. Instead of building classifiers one by one, multi-domain learning (MDL)
simultaneously builds classifiers on multiple domains. MDL utilizes the
information shared among the domains to improve the performance. As a
supervised learning problem, the labeling effort is still high in MDL problems.
Usually, this high labeling cost issue could be relieved by using active
learning. Thus, it is natural to utilize active learning to reduce the labeling
effort in MDL, and we refer this setting as multi-domain active learning
(MDAL). However, there are only few works which are built on this setting. And
when the researches have to face this problem, there is no off-the-shelf
solutions. Under this circumstance, combining the current multi-domain learning
models and single-domain active learning strategies might be a preliminary
solution for MDAL problem. To find out the potential of this preliminary
solution, a comparative study over 5 models and 4 selection strategies is made
in this paper. To the best of our knowledge, this is the first work provides
the formal definition of MDAL. Besides, this is the first comparative work for
MDAL problem. From the results, the Multinomial Adversarial Networks (MAN)
model with a simple best vs second best (BvSB) uncertainty strategy shows its
superiority in most cases. We take this combination as our off-the-shelf
recommendation for the MDAL problem