Open-Source Face Recognition Frameworks: A Review of the Landscape

Why is this an anomaly? Explaining anomalies using sequential explanations

Abstract In most applications, anomaly detection operates in an unsupervised mode by looking for outliers hoping that they are anomalies. Unfortunately, most anomaly detectors do not come with explanations about which features make a detected outlier point anomalous. Therefore, it requires human analysts to manually browse through each detected outlier point’s feature space to obtain the subset of features that will help them determine whether they are genuinely anomalous or not. This paper introduces sequential explanation (SE) methods that sequentially explain to the analyst which features make the detected outlier anomalous. We present two methods for computing SEs called the outlier and…

CArDIS : A Swedish Historical Handwritten Character and Word Dataset

This paper introduces a new publicly available image-based Swedish historical handwritten character and word dataset named Character Arkiv Digital Sweden (CArDIS) (https://cardisdataset.github.io/CARDIS/). The samples in CArDIS are collected from 64, 084 Swedish historical documents written by several anonymous priests between 1800 and 1900. The dataset contains 116, 000 Swedish alphabet images in RGB color space with 29 classes, whereas the word dataset contains 30, 000 image samples of ten popular Swedish names as well as 1, 000 region names in Sweden. To examine the performance of different machine learning classifiers on CArDIS dataset, three different experiments are conducted. In the …

Model-Agnostic Counterfactual Explanations in Credit Scoring

The past decade has shown a surge in the use and application of machine learning and deep learning models across different domains. One such domain is the credit scoring domain, where applicants are scored to assess their credit-worthiness for loan applications. During the scoring process, it is key to assure that there are no biases or discriminations that are incurred. Despite the proliferation of machine learning and deep learning models (referred to as black-box models in the literature) in credit scoring, there is still a need to explain how each prediction is made by the black-box models. Most of the machine learning and deep learning models are likely to be prone to unintended bias a…

Automatic Relative Radiometric Normalization of Bi-Temporal Satellite Images Using a Coarse-to-Fine Pseudo-Invariant Features Selection and Fuzzy Integral Fusion Strategies

Relative radiometric normalization (RRN) is important for pre-processing and analyzing multitemporal remote sensing (RS) images. Multitemporal RS images usually include different land use/land cover (LULC) types; therefore, considering an identical linear relationship during RRN modeling may result in potential errors in the RRN results. To resolve this issue, we proposed a new automatic RRN technique that efficiently selects the clustered pseudo-invariant features (PIFs) through a coarse-to-fine strategy and uses them in a fusion-based RRN modeling approach. In the coarse stage, an efficient difference index was first generated from the down-sampled reference and target images by combining…