An interactive image retrieval system learns which images in the database
belong to a user's query concept, by analyzing the example images and feedback
provided by the user. The challenge is to retrieve the relevant images with
minimal user interaction. In this work, we propose to solve this problem by
posing it as a binary classification task of classifying all images in the
database as being relevant or irrelevant to the user's query concept. Our
method combines active learning with graph-based semi-supervised learning
(GSSL) to tackle this problem. Active learning reduces the number of user
interactions by querying the labels of the most informative points and GSSL
allows to use abundant unlabeled data along with the limited labeled data
provided by the user. To efficiently find the most informative point, we use an
uncertainty sampling based method that queries the label of the point nearest
to the decision boundary of the classifier. We estimate this decision boundary
using our heuristic of adaptive threshold. To utilize huge volumes of unlabeled
data we use an efficient approximation based method that reduces the complexity
of GSSL from $O(n^3)$ to $O(n)$, making GSSL scalable. We make the classifier
robust to the diversity and noisy labels associated with images in large
databases by incorporating information from multiple modalities such as visual
information extracted from deep learning based models and semantic information
extracted from the WordNet. High F1 scores within few relevance feedback rounds
in our experiments with concepts defined on AnimalWithAttributes and Imagenet
(1.2 million images) datasets indicate the effectiveness and scalability of our
approach.

本文介绍了一种基于二分类算法的交互式图像检索系统，该系统结合了主动学习和基于图的半监督学习算法。该方法使用不确定性采样的方法来找到具有信息价值的点，并借助一个启发式自适应阈值来确定决策界限。同时使用从深度学习和 WordNet 中提取的视觉和语义信息，以提高分类器的鲁棒性。该方法在多个数据集上的实验中得到了较高的 F1 分数。

利用大规模无标注数据进行快速交互式图像检索

Fast Interactive Image Retrieval using large-scale unlabeled data

This paper is concerned with pool-based active learning for deep neural
networks. Motivated by coreset dataset compression ideas, we present a novel
active learning algorithm that queries consecutive points from the pool using
farthest-first traversals in the space of neural activation over a
representation layer. We show consistent and overwhelming improvement in sample
complexity over passive learning (random sampling) for three datasets: MNIST,
CIFAR-10, and CIFAR-100. In addition, our algorithm outperforms the traditional
uncertainty sampling technique (obtained using softmax activations), and we
identify cases where uncertainty sampling is only slightly better than random
sampling.

本文研究了基于池化的主动学习方法，提出了一种新的算法，使用神经网络在表征层上的激活空间中的最远优先遍历，从池中查询连续的点，相比于随机抽样和传统的不确定性抽样技术，对 MNIST、CIFAR-10 和 CIFAR-100 三个数据集的样本复杂度都有显著提高。

长尾深度主动学习

Deep Active Learning over the Long Tail

Logistic regression is by far the most widely used classifier in real-world
applications. In this paper, we benchmark the state-of-the-art active learning
methods for logistic regression and discuss and illustrate their underlying
characteristics. Experiments are carried out on three synthetic datasets and 44
real-world datasets, providing insight into the behaviors of these active
learning methods with respect to the area of the learning curve (which plots
classification accuracy as a function of the number of queried examples) and
their computational costs. Surprisingly, one of the earliest and simplest
suggested active learning methods, i.e., uncertainty sampling, performs
exceptionally well overall. Another remarkable finding is that random sampling,
which is the rudimentary baseline to improve upon, is not overwhelmed by
individual active learning techniques in many cases.

本文介绍了最常用的分类器之一 - 逻辑回归的主要应用和性能，比较了不同的主动学习方法在准确性和计算成本方面的表现，并且发现最简单和最早期的主动学习方法 - 不确定性采样，表现优越。

逻辑回归主动学习的基准和比较

A Benchmark and Comparison of Active Learning for Logistic Regression

The ability to cheaply train text classifiers is critical to their use in
information retrieval, content analysis, natural language processing, and other
tasks involving data which is partly or fully textual. An algorithm for
sequential sampling during machine learning of statistical classifiers was
developed and tested on a newswire text categorization task. This method, which
we call uncertainty sampling, reduced by as much as 500-fold the amount of
training data that would have to be manually classified to achieve a given
level of effectiveness.

通过未分类数据顺序抽样来训练文本分类器可以大大降低所需的训练数据量，提高训练效率。