Essays about: "Out-of-distribution detection"
Showing result 6 - 10 of 11 essays containing the words Out-of-distribution detection.
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6. Non-Bayesian Out-of-Distribution Detection Applied to CNN Architectures for Human Activity Recognition
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Human Activity Recognition (HAR) field studies the application of artificial intelligence methods for the identification of activities performed by people. Many applications of HAR in healthcare and sports require the safety-critical performance of the predictive models. READ MORE
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7. Overcoming generative likelihood bias for voxel-based out-of-distribution detection
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Deep learning-based dose prediction is a promising approach to automated radiotherapy planning but carries with it the risk of failing silently when the inputs are highly abnormal compared to the training data. One way to address this issue is to develop a dedicated outlier detector capable of detecting anomalous patient geometries. READ MORE
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8. Evaluating Unsupervised Methods for Out-of-Distribution Detection on Semantically Similar Image Data
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Out-of-distribution detection considers methods used to detect data that deviates from the underlying data distribution used to train some machine learning model. This is an important topic, as artificial neural networks have previously been shown to be capable of producing arbitrarily confident predictions, even for anomalous samples that deviate from the training distribution. READ MORE
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9. Impact of Semantic Segmentation on OOD Detection Performance for VAEs and Normalizing Flow Models
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : To achieve a higher grade of reliability among deep learning models, OOD (Out-Of-Distribution) detection has become an increasingly more prominent research field. What OOD detection does is to make the model figure out if inputted data is data it is meant to be trained on, or if it is data from a new distribution, giving the model a sense of its own ignorance. READ MORE
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10. Detecting Synthetic Images of Faces using Deep Learning
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Significant progress has been made within human face synthesis due to recent advances in generative adversarial networks. These networks can be used to generate credible high-quality images of faces not belonging to real people, which is something that could be exploited by malicious actors. READ MORE
