Self-supervised learning approaches leverage unlabeled samples to acquire generic
knowledge about different concepts, hence allowing for annotation-efficient downstream …

Autoencoder, as an essential part of many anomaly detection methods, is lacking flexibility
on normal data in complex datasets. U-Net is proved to be effective for this purpose but …

Molecular similarity is an elusive but core “unsupervised” cheminformatics concept, yet
different “fingerprint” encodings of molecular structures return very different similarity values …

Class imbalance frequently arises in the context of image classification. Conventional
generative adversarial networks (GANs) have a tendency to produce samples from the …

Class imbalance occurs in many real-world applications, including image classification,
where the number of images in each class differs significantly. With imbalanced data, the …

Abstract Using Variational Auto-Encoder (VAE) to learn disentangled representation holds
great promise. But there is a feature imbalance in the learning process of VAEs, and the …

The majority of existing post-hoc explanation approaches for machine learning models
produce independent, per-variable feature attribution scores, ignoring a critical inherent …

In the context of image generation, class imbalance often poses a challenge. Conventional
generative adversarial networks (GANs) tend to generate samples predominantly from the …

It is well known that Neural Network (network) performance often degrades when a network
is used in novel operating domains that differ from its training and testing domains. This is a …

Neural Networks are being integrated into safety critical systems, eg, perception systems for
autonomous vehicles, which require trained networks to perform safely in novel scenarios. It …