To achieve state-of-the-art performance, one still needs to train NER models
on large-scale, high-quality annotated data, an asset that is both costly and
time-intensive to accumulate. In contrast, real-world applications often resort
to massive low-quality labeled data through non-expert annotators via
crowdsourcing and external knowledge bases via distant supervision as a
cost-effective alternative. However, these annotation methods result in noisy
labels, which in turn lead to a notable decline in performance. Hence, we
propose to denoise the noisy NER data with guidance from a small set of clean
instances. Along with the main NER model we train a discriminator model and use
its outputs to recalibrate the sample weights. The discriminator is capable of
detecting both span and category errors with different discriminative prompts.
Results on public crowdsourcing and distant supervision datasets show that the
proposed method can consistently improve performance with a small guidance set.

为了实现最先进的性能，我们提出了使用少量干净实例的指导来去噪 NER 数据的方法，通过训练鉴别模型并使用其输出来重新校准样本权重，能够改善性能并在众包和远程监督数据集上保持一致的结果。

用少量干净实例改进在嘈杂数据上训练的命名实体识别器

Improving a Named Entity Recognizer Trained on Noisy Data with a Few  Clean Instances

Training Generative Adversarial Networks (GANs) remains a challenging
problem. The discriminator trains the generator by learning the distribution of
real/generated data. However, the distribution of generated data changes
throughout the training process, which is difficult for the discriminator to
learn. In this paper, we propose a novel method for GANs from the viewpoint of
online continual learning. We observe that the discriminator model, trained on
historically generated data, often slows down its adaptation to the changes in
the new arrival generated data, which accordingly decreases the quality of
generated results. By treating the generated data in training as a stream, we
propose to detect whether the discriminator slows down the learning of new
knowledge in generated data. Therefore, we can explicitly enforce the
discriminator to learn new knowledge fast. Particularly, we propose a new
discriminator, which automatically detects its retardation and then dynamically
masks its features, such that the discriminator can adaptively learn the
temporally-vary distribution of generated data. Experimental results show our
method outperforms the state-of-the-art approaches.

从在线持续学习的角度出发，提出了一种新颖的 GAN 方法，通过将生成的数据视为流来训练鉴别器模型，并自动检测其阻塞并动态遮罩其特征，使其能够适应临时变化的生成数据分布。实验结果表明，该方法优于现有的最先进方法。

用于生成对抗网络的动态遮挡鉴别器

Dynamically Masked Discriminator for Generative Adversarial Networks

Our ability to synthesize sensory data that preserves specific statistical
properties of the real data has had tremendous implications on data privacy and
big data analytics. The synthetic data can be used as a substitute for
selective real data segments,that are sensitive to the user, thus protecting
privacy and resulting in improved analytics.However, increasingly adversarial
roles taken by data recipients such as mobile apps, or other cloud-based
analytics services, mandate that the synthetic data, in addition to preserving
statistical properties, should also be difficult to distinguish from the real
data. Typically, visual inspection has been used as a test to distinguish
between datasets. But more recently, sophisticated classifier models
(discriminators), corresponding to a set of events, have also been employed to
distinguish between synthesized and real data. The model operates on both
datasets and the respective event outputs are compared for consistency. In this
paper, we take a step towards generating sensory data that can pass a deep
learning based discriminator model test, and make two specific contributions:
first, we present a deep learning based architecture for synthesizing sensory
data. This architecture comprises of a generator model, which is a stack of
multiple Long-Short-Term-Memory (LSTM) networks and a Mixture Density Network.
second, we use another LSTM network based discriminator model for
distinguishing between the true and the synthesized data. Using a dataset of
accelerometer traces, collected using smartphones of users doing their daily
activities, we show that the deep learning based discriminator model can only
distinguish between the real and synthesized traces with an accuracy in the
neighborhood of 50%.

本研究提出一种基于深度学习的神经网络生成器和鉴别器的架构，用于合成安全的人类感官数据，以保护隐私和改善大型数据分析，并提出了一种基于 LSTM 神经网络分辨真实和合成迹线的方法。实验结果表明，基于深度学习的鉴别器模型的准确性只有 50％左右。