Automated seismic fault interpretation has been an active area of research. Since 2018,
Deep learning (DL) based seismic fault interpretation methods have emerged and shown …

Recent advances in semi-supervised semantic segmentation have been heavily reliant on
pseudo labeling to compensate for limited labeled data, disregarding the valuable relational …

In semi-supervised learning (SSL), a common practice is to learn consistent information from
unlabeled data and discriminative information from labeled data to ensure both the …

This paper studies the problem of semi-supervised 2D-3D retrieval which aims to align both
labeled and unlabeled 2D and 3D data into the same embedding space. The problem is …

The recently proposed FixMatch and FlexMatch have achieved remarkable results in the
field of semi-supervised learning. But these two methods go to two extremes as FixMatch …

Label hierarchies are often available apriori as part of biological taxonomy or language
datasets WordNet. Several works exploit these to learn hierarchy aware features in order to …

Label hierarchy is an important source of external knowledge that can enhance
classification performance. However, most existing methods rely on predefined label …

Semi-supervised learning (SSL), which leverages a small number of labeled data that rely
on expert knowledge and a large number of easily accessible unlabeled data, has made …

Continual learning (CL) enables models to adapt to new tasks and environments without
forgetting previously learned knowledge. While current CL setups have ignored the …

Few-shot learning is proposed to tackle the problem of scarce training data in novel classes.
However, prior works in instance-level few-shot learning have paid less attention to …