Text autoencoders are commonly used for conditional generation tasks such as style transfer. We propose methods which are plug and play, where any pretrained autoencoder can be used, and only require learning a mapping within the autoencoder's embedding space, training embedding-to-embedding (Emb2Emb). This reduces the need for labeled training data for the task and makes the training procedure more efficient. Crucial to the success of this method is a loss term for keeping the mapped embedding on the manifold of the autoencoder and a mapping which is trained to navigate the manifold by learning offset vectors. Evaluations on style transfer tasks both with and without sequence-to-sequence supervision show that our method performs better than or comparable to strong baselines while being up to four times faster.
In this paper, an innovative solution is presented: a smart emotional system for impaired people's TV. It aims to accompany the cognitive information contained in a movie, with the affective content. The affect is then communicated to the movie viewers in ways compatible for people with hearing and/or visual impairments, to let them experience all of the sensations offered by the movie. To do so, emotion recognition techniques are used to classify movie scenes into seven basic emotions. These emotions are then represented, in realtime, while the movie is playing, to the viewers, using environmental lights, emotional subtitles and a second screen application that integrates vibrations, emoticons and background music.
The goal of the system presented in this demo is to make possible for the visually and hearing impaired audience to live empathetic viewing experiences using their home theatre. In this work we suggest the incorporation of new emotion communication modalities into the standard television, to provide the targeted audience with sensations that they do not have the opportunity to enjoy because of their disability.
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