… If so, we are currently changing a paradigm moving away from signal processing and
expert-crafted features into a highly … Lucic, “Recent advances in autoencoder-based …

… of speech. One review addresses speech representation learning based on deep learning
models [25], but does not address recent developments in self-supervised learning. This …

… The core idea leading to recent advances is inspired by metric learning as well as the work
in [37] and [38]. The idea is to not predict the exact class of the input but to instead predict …

… In this paper, we formulate and discuss a Contrastive Representation Learning (CRL)
framework… history and recent development of the contrastive approach in a wide range of domains. …

… representation learning and fusion can be applied to specific tasks, as well as a representation
of the current development of … recent progress in terms of developing representations for …

… new directions. Lastly, we propose possible research directions within LNRL, such as
new … We also envision potential directions beyond LNRL, such as learning with feature-noise, …

… Research on graph representation learning has received great attention in recent years …
into a low-dimensional vector representation while preserving the intrinsic graph properties. …

… Recent developments in computational power and the advent … as Computer Vision, Automatic
Speech Recognition, and in … learning becomes a crucial task for representation learning, …

… tasks. First, we present some classical models, followed by some famous representation
learning … such as image detection, speech recognition, NLP, and so on [76]. Continuous word …

… surveys on the latest developments in intelligent speech and vision applications from the …
yield human-level performance for simpler speech recognition tasks. For both CNNs and RNNs…