no code implementations • 18 May 2023 • Konstantinos Makantasis, Kosmas Pinitas, Antonios Liapis, Georgios N. Yannakakis
Privileged information enables affect models to be trained across multiple modalities available in a lab, and ignore, without significant performance drops, those modalities that are not available when they operate in the wild.
no code implementations • 14 Oct 2022 • Konstantinos Makantasis, Kosmas Pinitas, Antonios Liapis, Georgios N. Yannakakis
In particular, we assume that the ground truth of affect can be found in the causal relationships between elicitation, manifestation and annotation that remain \emph{invariant} across tasks and participants.
1 code implementation • 25 Aug 2022 • Kosmas Pinitas, Konstantinos Makantasis, Antonios Liapis, Georgios N. Yannakakis
Affect modeling is viewed, traditionally, as the process of mapping measurable affect manifestations from multiple modalities of user input to affect labels.
no code implementations • 14 Apr 2022 • Kosmas Pinitas, Konstantinos Makantasis, Antonios Liapis, Georgios N. Yannakakis
Stochastic gradient descent (SGD) is a premium optimization method for training neural networks, especially for learning objectively defined labels such as image objects and events.
no code implementations • 18 Oct 2021 • Kosmas Pinitas, Spyridon Chavlis, Panayiota Poirazi
Current deep learning architectures show remarkable performance when trained in large-scale, controlled datasets.