Machine learning has achieved remarkable success in many applications.
However, existing studies are largely based on the closed-world assumption,
which assumes that the environment is stationary, and the model is fixed once
deployed. In many real-world applications, this fundamental and rather naive
assumption may not hold because an open environment is complex, dynamic, and
full of unknowns. In such cases, rejecting unknowns, discovering novelties, and
then incrementally learning them, could enable models to be safe and evolve
continually as biological systems do. This paper provides a holistic view of
open-world machine learning by investigating unknown rejection, novel class
discovery, and class-incremental learning in a unified paradigm. The
challenges, principles, and limitations of current methodologies are discussed
in detail. Finally, we discuss several potential directions for future
research. This paper aims to provide a comprehensive introduction to the
emerging open-world machine learning paradigm, to help researchers build more
powerful AI systems in their respective fields, and to promote the development
of artificial general intelligence.

综合研究开放环境下机器学习的未知拒绝、新类别发现和类别自适应学习等方面，探讨了当前方法的挑战、原则和限制，以及未来研究的潜在方向。旨在全面介绍新兴的开放环境机器学习范式，帮助研究人员在各自领域构建更强大的人工智能系统，并促进人工通用智能的发展。

开放世界机器学习：回顾与新展望

Open-world Machine Learning: A Review and New Outlooks

Open-world machine learning (ML) combines closed-world models trained on
in-distribution data with out-of-distribution (OOD) detectors, which aim to
detect and reject OOD inputs. Previous works on open-world ML systems usually
fail to test their reliability under diverse, and possibly adversarial
conditions. Therefore, in this paper, we seek to understand how resilient are
state-of-the-art open-world ML systems to changes in system components? With
our evaluation across 6 OOD detectors, we find that the choice of
in-distribution data, model architecture and OOD data have a strong impact on
OOD detection performance, inducing false positive rates in excess of $70\%$.
We further show that OOD inputs with 22 unintentional corruptions or
adversarial perturbations render open-world ML systems unusable with false
positive rates of up to $100\%$. To increase the resilience of open-world ML,
we combine robust classifiers with OOD detection techniques and uncover a new
trade-off between OOD detection and robustness.

本文针对开放世界机器学习系统，测试其在各种条件下的可靠性，并发现在组件的选择方面，包括原始数据、模型体系结构和 OOD 数据等选择均严重影响 OOD 检测的性能，可能引起超过 70％的误检率，而对于包含 22 种非故意损坏或对抗性扰动的 OOD 输入，误报率可以达到 100％，并提出了结合鲁棒分类器和 OOD 检测技术来解决 OOD 检测和适应性之间的新的权衡。