Deepfake generation and detection: Case study and challenges
In smart communities, social media allowed users easy access to multimedia content. With
recent advancements in computer vision and natural language processing, machine …
recent advancements in computer vision and natural language processing, machine …
Cluster-based improved isolation forest
C Shao, X Du, J Yu, J Chen - Entropy, 2022 - mdpi.com
Outlier detection is an important research direction in the field of data mining. Aiming at the
problem of unstable detection results and low efficiency caused by randomly dividing …
problem of unstable detection results and low efficiency caused by randomly dividing …
Few-shot object detection: Application to medieval musicological studies
Detecting objects with a small representation in images is a challenging task, especially
when the style of the images is very different from recent photos, which is the case for …
when the style of the images is very different from recent photos, which is the case for …
Explaining bad forecasts in global time series models
Featured Application The outcomes of this work can be applied to understand better when
and why global time series forecasting models issue incorrect predictions and iteratively …
and why global time series forecasting models issue incorrect predictions and iteratively …
Adversarial reconstruction loss for domain generalization
The biggest fear when deploying machine learning models to the real world is their ability to
handle the new data. This problem is significant especially in medicine, where models …
handle the new data. This problem is significant especially in medicine, where models …
Generative adversarial network-based cross-project fault prediction
S Pal - arXiv preprint arXiv:2105.07207, 2021 - arxiv.org
Background: The early stage of defect prediction in the software development life cycle can
reduce testing effort and ensure the quality of software. Due to the lack of historical data …
reduce testing effort and ensure the quality of software. Due to the lack of historical data …
Adversarial domain adaptation for medieval instrument recognition
Image classification models have improved drastically due to neural networks. But as a
direct consequence of being trained on a specific dataset, neural networks tend to be biased …
direct consequence of being trained on a specific dataset, neural networks tend to be biased …
Unsupervised Anomaly Detection and Localization Based on Two-Hierarchy Normalizing Flow
Unsupervised anomaly detection (UAD) methods are widely used in industrial anomaly
detection, primarily since there is a lack of anomalous data available for training. However …
detection, primarily since there is a lack of anomalous data available for training. However …
Rule-Based Outlier Detection with a Modified Variational AutoEncoder for Enhancing Data Accuracy in Wireless Sensor Networks
In wireless sensor networks (WSNs), a number of outlier detection (OD) methods have been
established over time to identify data that do not match the rest of the data. These data are …
established over time to identify data that do not match the rest of the data. These data are …
Improving autoencoder-based outlier detection with adjustable probabilistic reconstruction error and mean-shift outlier scoring
Autoencoders were widely used in many machine learning tasks thanks to their strong
learning ability which has drawn great interest among researchers in the field of outlier …
learning ability which has drawn great interest among researchers in the field of outlier …