Method and apparatus for deep learning

A method for deep learning, comprising the following steps:

• receiving, by a deep learning model, a plurality of samples and a plurality of labels corresponding to the plurality of samples;
• adversarially augmenting, by the deep learning model, the plurality of samples based on a threat model;
• and
• assigning, by the deep learning model, a low predictive confidence to one or more adversarially augmented samples of the plurality of adversarially augmented samples having noisy labels due to the adversarially augmenting based on the threat model.

An apparatus for deep learning, comprising:

• a memory;
• and
• at least one processor coupled to the memory and configured to:
• receive, by a deep learning model, a plurality of samples and a plurality of labels corresponding to the plurality of samples;
• adversarially augment, by the deep learning model, the plurality of samples based on a threat model;
• and
• assign, by the deep learning model, a low predictive confidence to one or more adversarially augmented samples of the plurality of adversarially augmented samples having noisy labels due to the adversarially augmenting based on the threat model.

A non-transitory computer readable medium on which is stored computer code for deep learning, the computer code when executed by a processor, causing the processor to perform the following steps:

• receiving, by a deep learning model, a plurality of samples and a plurality of labels corresponding to the plurality of samples;
• adversarially augmenting, by the deep learning model, the plurality of samples based on a threat model;
• and
• assigning, by the deep learning model, a low predictive confidence to one or more adversarially augmented samples of the plurality of adversarially augmented samples having noisy labels due to the adversarially augmenting based on the threat model.

A method for visual recognition, comprising the following steps:

• receiving, by a deep learning model, a plurality of samples and a plurality of labels corresponding to the plurality of samples, wherein the deep learning model is configured to perform a visual recognition task and the plurality of samples include a plurality of images;
• adversarially augmenting, by the deep learning model, the plurality of samples based on a threat model;
• and
• assigning, by the deep learning model, a low predictive confidence to one or more adversarially augmented samples of the plurality of adversarially augmented samples having noisy labels due to the adversarially augmenting based on the threat model.