Generative adversarial networks (GANs) have recently become a hot research topic;
however, they have been studied since 2014, and a large number of algorithms have been …

Deep learning is transforming most areas of science and technology, including electron
microscopy. This review paper offers a practical perspective aimed at developers with …

Conditional GANs are at the forefront of natural image synthesis. The main drawback of such
models is the necessity for labeled data. In this work we exploit two popular unsupervised …

In this paper, we show that Generative Adversarial Networks (GANs) suffer from catastrophic
forgetting even when they are trained to approximate a single target distribution. We show …

We propose a few-shot learning method for detecting out-of-distribution (OOD) samples from
classes that are unseen during training while classifying samples from seen classes using …

As neural networks are increasingly being applied to real-world applications, mechanisms to
address distributional shift and sequential task learning without forgetting are critical …

Machine learning has witnessed tremendous growth in its adoption and advancement in the
last decade. The evolution of machine learning from traditional algorithms to modern deep …

Forgetting refers to the loss or deterioration of previously acquired information or knowledge.
While the existing surveys on forgetting have primarily focused on continual learning …

Abstract Data-free Knowledge Distillation (DFKD) has attracted attention recently thanks to
its appealing capability of transferring knowledge from a teacher network to a student …

Object detection is a challenging task, hindered by the scarcity of large annotated datasets.
In agriculture, the lack of annotated insect datasets often results in domain-specific models …