Modern machine learning research relies on relatively few carefully curated datasets.
Interestingly, despite deep feature extractors being inclined towards learning entangled features for skin lesion classification, individual features can still be decoded from this entangled representation.
The idea is to learn word representation by its surrounding words and utilize emojis in the text to help improve the classification results.
In this paper, we investigate the use of pretraining with adversarial networks, with the objective of discovering the relationship between network depth and robustness.
With the rise in edge-computing devices, there has been an increasing demand to deploy energy and resource-efficient models.
This paves the way for future research in the direction of adversarial attacks and defenses, particularly for time-series data.
For glaucoma classification we achieved AUC equal to 0. 874 which is 2. 7% relative improvement over the state-of-the-art results previously obtained for classification on ORIGA.
Identification of input data points relevant for the classifier (i. e. serve as the support vector) has recently spurred the interest of researchers for both interpretability as well as dataset debugging.
We approach the problem of interpretability in a novel way by proposing TSInsight where we attach an auto-encoder to the classifier with a sparsity-inducing norm on its output and fine-tune it based on the gradients from the classifier and a reconstruction penalty.
This indicates a vital gap between the explainability provided by the systems and the novice user.
The promise of ANNs to automatically discover and extract useful features/patterns from data without dwelling on domain expertise although seems highly promising but comes at the cost of high reliance on large amount of accurately labeled data, which is often hard to acquire and formulate especially in time-series domains like anomaly detection, natural disaster management, predictive maintenance and healthcare.
In contrast to the anomaly detection methods where anomalies are learned, DeepAnT uses unlabeled data to capture and learn the data distribution that is used to forecast the normal behavior of a time series.
Despite the great advances made by deep learning in many machine learning problems, there is a relative dearth of deep learning approaches for anomaly detection.
Ranked #28 on Anomaly Detection on One-class CIFAR-10
This is a step towards making informed/explainable decisions in the domain of time-series, powered by deep learning.