1,094 research outputs found
Adversarial Training in Affective Computing and Sentiment Analysis: Recent Advances and Perspectives
Over the past few years, adversarial training has become an extremely active
research topic and has been successfully applied to various Artificial
Intelligence (AI) domains. As a potentially crucial technique for the
development of the next generation of emotional AI systems, we herein provide a
comprehensive overview of the application of adversarial training to affective
computing and sentiment analysis. Various representative adversarial training
algorithms are explained and discussed accordingly, aimed at tackling diverse
challenges associated with emotional AI systems. Further, we highlight a range
of potential future research directions. We expect that this overview will help
facilitate the development of adversarial training for affective computing and
sentiment analysis in both the academic and industrial communities
Precipitation nowcasting with generative diffusion models
In recent years traditional numerical methods for accurate weather prediction
have been increasingly challenged by deep learning methods. Numerous historical
datasets used for short and medium-range weather forecasts are typically
organized into a regular spatial grid structure. This arrangement closely
resembles images: each weather variable can be visualized as a map or, when
considering the temporal axis, as a video. Several classes of generative
models, comprising Generative Adversarial Networks, Variational Autoencoders,
or the recent Denoising Diffusion Models have largely proved their
applicability to the next-frame prediction problem, and is thus natural to test
their performance on the weather prediction benchmarks. Diffusion models are
particularly appealing in this context, due to the intrinsically probabilistic
nature of weather forecasting: what we are really interested to model is the
probability distribution of weather indicators, whose expected value is the
most likely prediction.
In our study, we focus on a specific subset of the ERA-5 dataset, which
includes hourly data pertaining to Central Europe from the years 2016 to 2021.
Within this context, we examine the efficacy of diffusion models in handling
the task of precipitation nowcasting. Our work is conducted in comparison to
the performance of well-established U-Net models, as documented in the existing
literature. Our proposed approach of Generative Ensemble Diffusion (GED)
utilizes a diffusion model to generate a set of possible weather scenarios
which are then amalgamated into a probable prediction via the use of a
post-processing network. This approach, in comparison to recent deep learning
models, substantially outperformed them in terms of overall performance.Comment: 21 pages, 6 figure
- …