Noise is a fundamental problem in learning theory with huge effects in the
application of Machine Learning (ML) methods, due to real world data tendency
to be noisy. Additionally, introduction of malicious noise can make ML methods
fail critically, as is the case with adversarial attacks. Thus, finding and
developing alternatives to improve robustness to noise is a fundamental problem
in ML. In this paper, we propose a method to deal with noise: mitigating its
effect through the use of data abstractions. The goal is to reduce the effect
of noise over the model's performance through the loss of information produced
by the abstraction. However, this information loss comes with a cost: it can
result in an accuracy reduction due to the missing information. First, we
explored multiple methodologies to create abstractions, using the training
dataset, for the specific case of numerical data and binary classification
tasks. We also tested how these abstractions can affect robustness to noise
with several experiments that explore the robustness of an Artificial Neural
Network to noise when trained using raw data \emph{vs} when trained using
abstracted data. The results clearly show that using abstractions is a viable
approach for developing noise robust ML methods.

通过使用数据抽象来减少噪音对模型性能的影响是一种可行的方法，这可以增强机器学习算法对噪音的鲁棒性。

通过抽象提高噪声鲁棒性对机器学习的影响

Improving Noise Robustness through Abstractions and its Impact on  Machine Learning

Generative models are typically trained on grid-like data such as images. As
a result, the size of these models usually scales directly with the underlying
grid resolution. In this paper, we abandon discretized grids and instead
parameterize individual data points by continuous functions. We then build
generative models by learning distributions over such functions. By treating
data points as functions, we can abstract away from the specific type of data
we train on and construct models that are agnostic to discretization. To train
our model, we use an adversarial approach with a discriminator that acts on
continuous signals. Through experiments on a wide variety of data modalities
including images, 3D shapes and climate data, we demonstrate that our model can
learn rich distributions of functions independently of data type and
resolution.

以连续函数参数化单个数据点，用生成对抗学习方法训练生成模型，达到基于数据类型和分辨率的抽象和多模态学习。