430 research outputs found
A Survey on Fairness-aware Recommender Systems
As information filtering services, recommender systems have extremely
enriched our daily life by providing personalized suggestions and facilitating
people in decision-making, which makes them vital and indispensable to human
society in the information era. However, as people become more dependent on
them, recent studies show that recommender systems potentially own
unintentional impacts on society and individuals because of their unfairness
(e.g., gender discrimination in job recommendations). To develop trustworthy
services, it is crucial to devise fairness-aware recommender systems that can
mitigate these bias issues. In this survey, we summarise existing methodologies
and practices of fairness in recommender systems. Firstly, we present concepts
of fairness in different recommendation scenarios, comprehensively categorize
current advances, and introduce typical methods to promote fairness in
different stages of recommender systems. Next, after introducing datasets and
evaluation metrics applied to assess the fairness of recommender systems, we
will delve into the significant influence that fairness-aware recommender
systems exert on real-world industrial applications. Subsequently, we highlight
the connection between fairness and other principles of trustworthy recommender
systems, aiming to consider trustworthiness principles holistically while
advocating for fairness. Finally, we summarize this review, spotlighting
promising opportunities in comprehending concepts, frameworks, the balance
between accuracy and fairness, and the ties with trustworthiness, with the
ultimate goal of fostering the development of fairness-aware recommender
systems.Comment: 27 pages, 9 figure
Learning Fair Representations with High-Confidence Guarantees
Representation learning is increasingly employed to generate representations
that are predictive across multiple downstream tasks. The development of
representation learning algorithms that provide strong fairness guarantees is
thus important because it can prevent unfairness towards disadvantaged groups
for all downstream prediction tasks. To prevent unfairness towards
disadvantaged groups in all downstream tasks, it is crucial to provide
representation learning algorithms that provide fairness guarantees. In this
paper, we formally define the problem of learning representations that are fair
with high confidence. We then introduce the Fair Representation learning with
high-confidence Guarantees (FRG) framework, which provides high-confidence
guarantees for limiting unfairness across all downstream models and tasks, with
user-defined upper bounds. After proving that FRG ensures fairness for all
downstream models and tasks with high probability, we present empirical
evaluations that demonstrate FRG's effectiveness at upper bounding unfairness
for multiple downstream models and tasks
Self-supervised debiasing using low rank regularization
Spurious correlations can cause strong biases in deep neural networks,
impairing generalization ability. While most existing debiasing methods require
full supervision on either spurious attributes or target labels, training a
debiased model from a limited amount of both annotations is still an open
question. To address this issue, we investigate an interesting phenomenon using
the spectral analysis of latent representations: spuriously correlated
attributes make neural networks inductively biased towards encoding lower
effective rank representations. We also show that a rank regularization can
amplify this bias in a way that encourages highly correlated features.
Leveraging these findings, we propose a self-supervised debiasing framework
potentially compatible with unlabeled samples. Specifically, we first pretrain
a biased encoder in a self-supervised manner with the rank regularization,
serving as a semantic bottleneck to enforce the encoder to learn the spuriously
correlated attributes. This biased encoder is then used to discover and
upweight bias-conflicting samples in a downstream task, serving as a boosting
to effectively debias the main model. Remarkably, the proposed debiasing
framework significantly improves the generalization performance of
self-supervised learning baselines and, in some cases, even outperforms
state-of-the-art supervised debiasing approaches
Investigating Trade-offs For Fair Machine Learning Systems
Fairness in software systems aims to provide algorithms that operate in a nondiscriminatory manner, with respect to protected attributes such as gender, race,
or age. Ensuring fairness is a crucial non-functional property of data-driven Machine Learning systems. Several approaches (i.e., bias mitigation methods) have
been proposed in the literature to reduce bias of Machine Learning systems. However, this often comes hand in hand with performance deterioration. Therefore, this
thesis addresses trade-offs that practitioners face when debiasing Machine Learning
systems.
At first, we perform a literature review to investigate the current state of the
art for debiasing Machine Learning systems. This includes an overview of existing
debiasing techniques and how they are evaluated (e.g., how is bias measured).
As a second contribution, we propose a benchmarking approach that allows for
an evaluation and comparison of bias mitigation methods and their trade-offs (i.e.,
how much performance is sacrificed for improving fairness).
Afterwards, we propose a debiasing method ourselves, which modifies already
trained Machine Learning models, with the goal to improve both, their fairness and
accuracy.
Moreover, this thesis addresses the challenge of how to deal with fairness with
regards to age. This question is answered with an empirical evaluation on real-world
datasets
Explainable Artificial Intelligence for Image Segmentation and for Estimation of Optical Aberrations
State-of-the-art machine learning methods such as convolutional neural networks (CNNs) are frequently employed in computer vision. Despite their high performance on unseen data, CNNs are often criticized for lacking transparency — that is, providing very limited if any information about the internal decision-making process. In some applications, especially in healthcare, such transparency of algorithms is crucial for end users, as trust in diagnosis and prognosis is important not only for the satisfaction and potential adherence of patients, but also for their health. Explainable artificial intelligence (XAI) aims to open up this “black box,” often perceived as a cryptic and inconceivable algorithm, to increase understanding of the machines’ reasoning.XAI is an emerging field, and techniques for making machine learning explainable are becoming increasingly available. XAI for computer vision mainly focuses on image classification, whereas interpretability in other tasks remains challenging. Here, I examine explainability in computer vision beyond image classification, namely in semantic segmentation and 3D multitarget image regression.
This thesis consists of five chapters.
In Chapter 1 (Introduction), the background of artificial intelligence (AI), XAI, computer vision, and optics is presented, and the definitions of the terminology for XAI are proposed.
Chapter 2 is focused on explaining the predictions of U-Net, a CNN commonly used for semantic image segmentation, and variations of this architecture. To this end, I propose the gradient-weighted class activation mapping for segmentation (Seg-Grad-CAM) method based on the well-known Grad-CAM method for explainable image classification.
In Chapter 3, I present the application of deep learning to estimation of optical aberrations in microscopy biodata by identifying the present Zernike aberration modes and their amplitudes. A CNN-based approach PhaseNet can accurately estimate monochromatic aberrations in images of point light sources. I extend this method to objects of complex shapes.
In Chapter 4, an approach for explainable 3D multitarget image regression is reported. First, I visualize how the model differentiates the aberration modes using the local interpretable model-agnostic explanations (LIME) method adapted for 3D image classification. Then I “explain,” using LIME modified for multitarget 3D image regression (Image-Reg-LIME), the outputs of the regression model for estimation of the amplitudes.
In Chapter 5, the results are discussed in a broader context.
The contribution of this thesis is the development of explainability methods for semantic segmentation and 3D multitarget image regression of optical aberrations. The research opens the door for further enhancement of AI’s transparency.:Title Page i
List of Figures xi
List of Tables xv
1 Introduction 1
1.1 Essential Definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.1.1 Artificial intelligence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.1.2 Explainable . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.1.3 Proposed definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.2 Explainable Artificial Intelligence . . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.2.1 Aims and applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.2.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
1.3 Computer Vision . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
1.3.1 Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
1.3.2 Image classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
1.3.3 Image regression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
1.3.4 Image segmentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
1.4 Optics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
1.4.1 Aberrations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
1.4.2 Zernike polynomials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
1.5 Thesis Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
1.5.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
1.5.2 Dissertation outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
2 Explainable Image Segmentation 23
2.1 Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
2.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
2.3 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
2.3.1 CAM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
2.3.2 Grad-CAM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
2.3.3 U-Net . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
2.3.4 Seg-Grad-CAM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
2.4 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
2.4.1 Circles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
2.4.2 TextureMNIST . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
2.4.3 Cityscapes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
2.5 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
2.5.1 Circles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
2.5.2 TextureMNIST . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
2.5.3 Cityscapes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
2.6 Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
2.7 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
3 Estimation of Aberrations 55
3.1 Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
3.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
3.3 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
3.3.1 PhaseNet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
3.3.2 PhaseNet data generator . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
3.3.3 Retrieval of noise parameters . . . . . . . . . . . . . . . . . . . . . . . . 62
3.3.4 Data generator with phantoms . . . . . . . . . . . . . . . . . . . . . . . 62
3.3.5 Restoration via deconvolution . . . . . . . . . . . . . . . . . . . . . . . . 63
3.3.6 Convolution with the “zero” synthetic PSF . . . . . . . . . . . . . . . . 63
3.4 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
3.4.1 Astrocytes (synthetic data) . . . . . . . . . . . . . . . . . . . . . . . . . 65
3.4.2 Fluorescent beads . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
3.4.3 Drosophila embryo (live sample) . . . . . . . . . . . . . . . . . . . . . . 67
3.4.4 Neurons (fixed sample) . . . . . . . . . . . . . . . . . . . . . . . . . . . 68
3.5 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
3.5.1 Astrocytes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
3.5.2 Conclusions on the results for astrocytes . . . . . . . . . . . . . . . . . . 74
3.5.3 Fluorescent beads . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75
3.5.4 Conclusions on the results for fluorescent beads . . . . . . . . . . . . . . 81
3.5.5 Drosophila embryo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83
3.5.6 Conclusions on the results for Drosophila embryo . . . . . . . . . . . . . 87
3.5.7 Neurons . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88
3.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96
4 Explainable Multitarget Image Regression 99
4.1 Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99
4.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100
4.3 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102
4.3.1 LIME . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102
4.3.2 Superpixel algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104
4.3.3 LIME for 3D image classification . . . . . . . . . . . . . . . . . . . . . . 104
4.3.4 Image-Reg-LIME: LIME for 3D image regression . . . . . . . . . . . . . 107
4.4 Results: Classification of Aberrations . . . . . . . . . . . . . . . . . . . . . . . . 109
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TABLE OF CONTENTS
4.4.1 Transforming the regression task into classification . . . . . . . . . . . . 110
4.4.2 Data augmentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111
4.4.3 Parameter search . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112
4.4.4 Clustering of 3D images . . . . . . . . . . . . . . . . . . . . . . . . . . . 114
4.4.5 Explanations of classification . . . . . . . . . . . . . . . . . . . . . . . . 114
4.4.6 Conclusions on the results for classification . . . . . . . . . . . . . . . . 117
4.5 Results: Explainable Regression of Aberrations . . . . . . . . . . . . . . . . . . 118
4.5.1 Explanations with a reference value . . . . . . . . . . . . . . . . . . . . 121
4.5.2 Validation of explanations . . . . . . . . . . . . . . . . . . . . . . . . . . 122
4.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125
5 Conclusions and Outlook 127
References 12
A Debiasing Variational Autoencoder for Deforestation Mapping
Deep Learning (DL) algorithms provide numerous benefits in different applications, and they usually yield successful results in scenarios with enough labeled training data and similar class proportions. However, the labeling procedure is a cost and time-consuming task. Furthermore, numerous real-world classification problems present a high level of class imbalance, as the number of samples from the classes of interest differ significantly. In various cases, such conditions tend to promote the creation of biased systems, which negatively impact their performance. Designing unbiased systems has been an active research topic, and recently some DL-based techniques have demonstrated encouraging results in that regard. In this work, we introduce an extension of the Debiasing Variational Autoencoder (DB-VAE) for semantic segmentation. The approach is based on an end-to-end DL scheme and employs the learned latent variables to adjust the individual sampling probabilities of data points during the training process. For that purpose, we adapted the original DB-VAE architecture for dense labeling in the context of deforestation mapping. Experiments were carried out on a region of the Brazilian Amazon, using Sentinel-2 data and the deforestation map from the PRODES project. The reported results show that the proposed DB-VAE approach is able to learn and identify under-represented samples, and select them more frequently in the training batches, consequently delivering superior classification metrics
A Survey of Dataset Refinement for Problems in Computer Vision Datasets
Large-scale datasets have played a crucial role in the advancement of
computer vision. However, they often suffer from problems such as class
imbalance, noisy labels, dataset bias, or high resource costs, which can
inhibit model performance and reduce trustworthiness. With the advocacy of
data-centric research, various data-centric solutions have been proposed to
solve the dataset problems mentioned above. They improve the quality of
datasets by re-organizing them, which we call dataset refinement. In this
survey, we provide a comprehensive and structured overview of recent advances
in dataset refinement for problematic computer vision datasets. Firstly, we
summarize and analyze the various problems encountered in large-scale computer
vision datasets. Then, we classify the dataset refinement algorithms into three
categories based on the refinement process: data sampling, data subset
selection, and active learning. In addition, we organize these dataset
refinement methods according to the addressed data problems and provide a
systematic comparative description. We point out that these three types of
dataset refinement have distinct advantages and disadvantages for dataset
problems, which informs the choice of the data-centric method appropriate to a
particular research objective. Finally, we summarize the current literature and
propose potential future research topics.Comment: 33 pages, 10 figures, to be published in ACM Computing Survey
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