Detecting and Learning Out-of-Distribution Data in the Open world: Algorithm and Theory

TL;DR

This paper explores algorithms and theoretical foundations for detecting out-of-distribution data and learning from new classes in open-world machine learning scenarios, addressing the limitations of traditional closed-world models.

Contribution

It introduces novel methods and theoretical insights for OOD detection and open-world representation learning, enabling models to identify and incorporate unknown data effectively.

Findings

Proposed algorithms improve OOD detection accuracy.

Theoretical analysis supports the effectiveness of the methods.

Enhanced open-world learning capabilities demonstrated.

Abstract

This thesis makes considerable contributions to the realm of machine learning, specifically in the context of open-world scenarios where systems face previously unseen data and contexts. Traditional machine learning models are usually trained and tested within a fixed and known set of classes, a condition known as the closed-world setting. While this assumption works in controlled environments, it falls short in real-world applications where new classes or categories of data can emerge dynamically and unexpectedly. To address this, our research investigates two intertwined steps essential for open-world machine learning: Out-of-distribution (OOD) Detection and Open-world Representation Learning (ORL). OOD detection focuses on identifying instances from unknown classes that fall outside the model's training distribution. This process reduces the risk of making overly confident, erroneous predictions about unfamiliar inputs. Moving beyond OOD detection, ORL extends the capabilities of the model to not only detect unknown instances but also learn from and incorporate knowledge about these new classes. By delving into these research problems of open-world learning, this thesis contributes both algorithmic solutions and theoretical foundations, which pave the way for building machine learning models that are not only performant but also reliable in the face of the evolving complexities of the real world.