Prioritizing Informative Features and Examples for Deep Learning from Noisy Data: A Ph.D. Dissertation by Dongmin Park

Concetti Chiave

The author proposes a systematic framework to prioritize informative features and examples throughout the model development process, aiming to mitigate the negative impact of noisy data on deep learning applications.

Sintesi

In this dissertation, Dongmin Park addresses the challenge of noisy data in deep learning by focusing on prioritizing informative features and examples. The study emphasizes the importance of distinguishing between informative and noisy elements to enhance model performance. By proposing innovative approaches for feature learning, active learning, and data pruning, the research aims to create a robust system that effectively handles noise in real-world applications.
Key points:

DNNs tend to memorize noise leading to performance degradation.
The study focuses on removing noisy features' contribution through feature debiasing.
Active learning is utilized to reduce labeling costs by prioritizing informative examples.
OOD detection methods are explored for open-set recognition.
The dissertation outlines a comprehensive approach to address noise in different stages of model development.

Statistiche

DNNs tend to overly capture all available signals from training data even when they are not essential for solving a given task [11, 12].
DNNs are easily deceived by adversarial perturbations of the inputs, so-called adversarial examples [19].
Numerous studies have attempted to prevent overfitting to the undesirable features in standard supervised learning tasks.
Active Learning (AL) is a learning framework to reduce the human labeling cost by finding the most informative examples given unlabeled data [25, 26].

Citazioni

"DNNs tend to overly capture all available signals from training data even when they are not essential for solving a given task."
"DNNs are easily deceived by adversarial perturbations of the inputs, so-called adversarial examples."
"Active Learning (AL) is a learning framework to reduce the human labeling cost by finding the most informative examples given unlabeled data."

Approfondimenti chiave tratti da

Prioritizing Informative Features and Examples for Deep Learning from Noisy Data

by Dongmin Park alle arxiv.org 03-04-2024

https://arxiv.org/pdf/2403.00013.pdf

Domande più approfondite

How can prioritizing informative features impact real-world applications beyond deep learning

Prioritizing informative features can have a significant impact on real-world applications beyond deep learning. In fields like healthcare, identifying relevant features in medical data can lead to more accurate diagnoses and treatment plans for patients. For example, in cancer detection, prioritizing informative features can help distinguish between benign and malignant tumors with higher accuracy, leading to better patient outcomes.
In finance, prioritizing informative features can improve fraud detection systems by focusing on key indicators of fraudulent activity. This can help financial institutions prevent monetary losses and protect their customers from potential scams.
Moreover, in autonomous vehicles, prioritizing informative features such as road signs, pedestrian movements, and traffic patterns is crucial for ensuring the safety and efficiency of self-driving cars. By filtering out noisy or irrelevant information from sensors and cameras, autonomous vehicles can make better decisions in real-time scenarios.
Overall, prioritizing informative features not only enhances the performance of machine learning models but also has practical implications across various industries where data-driven decision-making is essential.

What counterarguments exist against removing noisy features' contribution through feature debiasing

While feature debiasing through the removal of noisy feature contribution has its advantages in improving model generalization and reducing overfitting to irrelevant signals in the data, there are some counterarguments against this approach:

Loss of Information: Removing noisy feature contributions may inadvertently eliminate valuable information that could be useful for certain edge cases or specific scenarios. It's important to strike a balance between removing noise and retaining potentially useful but non-essential features.

Complexity: Feature debiasing methods often add complexity to the model training process by introducing additional regularization techniques or preprocessing steps. This complexity may hinder interpretability and increase computational overhead.

Domain Shift: The removal of noisy feature contributions based on OOD examples assumes that these examples adequately represent all possible variations outside the target domain. However, OOD examples may not always capture all aspects of external data distributions accurately.

Ethical Considerations: There might be ethical concerns related to bias removal if it leads to unintended consequences or reinforces existing biases unknowingly.

How does OOD detection contribute to open-set recognition beyond traditional methods

OOD detection plays a crucial role in open-set recognition by enabling models to differentiate between known classes (in-distribution) and unknown classes (out-of-distribution). Beyond traditional methods like uncertainty-based scoring functions or density-based approaches used for OOD detection:

Improved Robustness: OOD detection helps enhance model robustness by flagging instances that deviate significantly from the training distribution during inference.

2 .Enhanced Generalization: By incorporating OOD detection mechanisms into open-set recognition tasks,
models become more adept at handling novel inputs that were not present during training.
3 .Reduced False Positives: Effective OOD detection reduces false positives by minimizing misclassifications
of unseen samples as belonging to known classes.
4 .Adversarial Defense: Leveraging advanced techniques such as adversarial training alongside OOD
detection further fortifies models against adversarial attacks aimed at exploiting vulnerabilities due
to uncertain predictions.
These advancements contribute towards building more reliable AI systems capable of handling diverse real-world scenarios effectively while mitigating risks associated with encountering unknown inputs during deployment.