Active learning (AL) aims to minimize labeling efforts for data-demanding
deep neural networks (DNNs) by selecting the most representative data points
for annotation. However, currently used methods are ill-equipped to deal with
biased data. The main motivation of this paper is to consider a realistic
setting for pool-based semi-supervised AL, where the unlabeled collection of
train data is biased. We theoretically derive an optimal acquisition function
for AL in this setting. It can be formulated as distribution shift minimization
between unlabeled train data and weakly-labeled validation dataset. To
implement such acquisition function, we propose a low-complexity method for
feature density matching using self-supervised Fisher kernel (FK) as well as
several novel pseudo-label estimators. Our FK-based method outperforms
state-of-the-art methods on MNIST, SVHN, and ImageNet classification while
requiring only 1/10th of processing. The conducted experiments show at least
40% drop in labeling efforts for the biased class-imbalanced data compared to
existing methods.

本文提出了一种用于半监督主动学习的数据获取函数，通过考虑训练数据集的分布移位来解决数据偏倚问题，并使用自监督 Fisher kernel 来进行特征匹配，实现了对 MNIST、SVHN 和 ImageNet 分类的优化，并且与现有的方法相比可以减少至少 40% 的标注工作量。

通过 Fisher Kernel 自监督的深度主动学习用于偏置数据集

Deep Active Learning for Biased Datasets via Fisher Kernel  Self-Supervision

Tandem mass spectrometry (MS/MS) is a high-throughput technology used
toidentify the proteins in a complex biological sample, such as a drop of
blood. A collection of spectra is generated at the output of the process, each
spectrum of which is representative of a peptide (protein subsequence) present
in the original complex sample. In this work, we leverage the log-likelihood
gradients of generative models to improve the identification of such spectra.
In particular, we show that the gradient of a recently proposed dynamic
Bayesian network (DBN) may be naturally employed by a kernel-based
discriminative classifier. The resulting Fisher kernel substantially improves
upon recent attempts to combine generative and discriminative models for
post-processing analysis, outperforming all other methods on the evaluated
datasets. We extend the improved accuracy offered by the Fisher kernel
framework to other search algorithms by introducing Theseus, a DBN representing
a large number of widely used MS/MS scoring functions. Furthermore, with
gradient ascent and max-product inference at hand, we use Theseus to learn
model parameters without any supervision.

本文基于生成对抗生成模型的对数似然梯度，介绍利用一种基于动态贝叶斯网络（DBN）的核判别分类器改进 Tandem Mass Spectrometry 中蛋白质识别的方法，通过引入 Theseus DBN 搜索算法，实现了自动学习模型参数的功能，并在评估数据集上取得了最优效果的结果。