Patent ID: 11886480
Assignee: ADOBE INC.
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 7:
8. The method of claim 1, wherein the gated convolutional encoder-decoder model is jointly trained using a weighted auto-encoder loss associated with a reconstruction engine and a weighted classification loss associated with the supervised classification engine, wherein jointly training the gated convolutional encoder-decoder model using the weighted classification loss comprises:
extracting training linguistic features from training data into a training linguistic feature vector representation;
encoding the training data to generate a training latent representation of the training data;
normalizing and concatenating the training linguistic feature vector representation with the training latent representation to generate a training appended latent representation;
providing the training appended latent representation to a set of fully connected layers with a Softmax classifier loss layer to generate a predicted affect classification of the training data;
comparing the predicted affect classification of the training data to a ground-truth label of the training data; and
training the gated convolutional encoder-decoder model using the weighted classification loss based on a comparison between the predicted affect classification of the training data and the ground-truth label of the training data.