System and Method For Pseudo-Task Augmentation in Deep Multitask Learning

A neural network-implemented method, including:

• processing an input through an encoder and generating an encoding;
• processing the encoding through numerous decoders and producing classification scores for classes defined for corresponding classification tasks, wherein the decoders are grouped into sets of decoders in dependence upon the corresponding classification tasks and respectively receive the encoding as input from the encoder, thereby forming encoder-decoder pairs which operate independently of each other;
• and
• jointly training the encoder-decoder pairs over one thousand to millions of backpropagation iterations to perform the corresponding classification tasks.

A non-transitory computer readable storage medium impressed with computer program instructions, which, when executed on a processor, implement a method comprising:

• processing an input through an encoder and generating an encoding;
• processing the encoding through numerous decoders and producing classification scores for classes defined for corresponding classification tasks, wherein the decoders are grouped into sets of decoders in dependence upon the corresponding classification tasks and respectively receive the encoding as input from the encoder, thereby forming encoder-decoder pairs which operate independently of each other;
• and
• jointly training the encoder-decoder pairs over one thousand to millions of backpropagation iterations to perform the corresponding classification tasks.

A pseudo-task augmentation system, comprising:

• an underlying multitask model that embeds task inputs into task embeddings;
• a plurality of decoder models that project the task embeddings into distinct classification layers;
• wherein a combination of the multitask model and a decoder model in the plurality of decoder models defines a task model, and a plurality of task models populate a model space;
• and
• a traverser that traverses a model space and determines a distinct loss for each task model in the model based on a distinct gradient during training.