Research|
Out-of-distribution detection methods on deep neural network encodings of images and tabular data
The performance of supervised learning models trained on time-series data can be affected by changing phenomena and drifts. Our prior work in this area focused on detecting outliers in tabular datasets derived from financial time series, yielding promising outcomes but revealing limitations. This project aimed to improve on this by operating in the latent space of deep neural networks, detecting anomalies in the model's internal data representation compared to the learned representations from training data. This shift emphasizes assessing whether the model's data representation is anomalous rather than the input data itself.