A Conformal Mapping-based Framework for Robot-to-Robot and Sim-to-Real Transfer Learning

Gao, Shijie; Bezzo, Nicola

doi:10.1109/iros51168.2021.9636682

Cited by 1 publication

(1 citation statement)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…While the source and target domains may have differences between them in their original data space, it is likely that the two would exhibit similarities in a transformed data space. Mappingbased deep transfer learning techniques, such as Transfer Component Analysis [423], create a union between the source and target domain instances by applying a mapping between the two and transforming them into a new data space based on their similarity so that they can be used for deep nets [419], [424].…”

Section: ) Instance-basedmentioning

confidence: 99%

Deep Representation Learning: Fundamentals, Technologies, Applications, and Open Challenges

Payandeh,

Baghaei,

Fayyazsanavi

et al. 2023

IEEE Access

View full text Add to dashboard Cite

Machine learning algorithms have had a profound impact on the field of computer science over the past few decades. The performance of these algorithms heavily depends on the representations derived from the data during the learning process. Successful learning processes aim to produce concise, discrete, meaningful representations that can be effectively applied to various tasks. Recent advancements in deep learning models have proven to be highly effective in capturing high-dimensional, non-linear, and multi-modal characteristics. In this work, we provide a comprehensive overview of the current state-of-the-art in deep representation learning and the principles and developments made in the process of representation learning. Our study encompasses both supervised and unsupervised methods, including popular techniques such as autoencoders, self-supervised methods, and deep neural networks. Furthermore, we explore a wide range of applications, including image recognition and natural language processing. In addition, we discuss recent trends, key issues, and open challenges in the field. This survey endeavors to make a significant contribution to the field of deep representation learning, fostering its understanding and facilitating further advancements.

show abstract

Section: ) Instance-basedmentioning

confidence: 99%