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Found 7 papers, 2 papers with code
Visual Representation Learning over Latent Domains
A fundamental shortcoming of deep neural networks is their specialization to a single task and domain.
Reducing Implicit Bias in Latent Domain Learning
A fundamental shortcoming of deep neural networks is their specialization to a single task and domain.
Deep Anomaly Detection by Residual Adaptation
Deep anomaly detection is a difficult task since, in high dimensions, it is hard to completely characterize a notion of "differentness" when given only examples of normality.
Latent Domain Learning with Dynamic Residual Adapters
While recent techniques in domain adaptation and multi-domain learning enable the learning of more domain-agnostic features, their success relies on the presence of domain labels, typically requiring manual annotation and careful curation of datasets.
Deep One-Class Classification
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 #32 on
Anomaly Detection
on One-class CIFAR-10
Anomaly Detection with Generative Adversarial Networks
Many anomaly detection methods exist that perform well on low-dimensional problems however there is a notable lack of effective methods for high-dimensional spaces, such as images.
