Systems, methods, and apparatus for image classification with domain invariant regularization

1 . A method comprising:

receiving an input image;

using a domain invariant machine learning model to compute an output based on the input image, wherein the domain invariant machine learning model is trained using domain invariant regularization comprising a style diversification loss function, wherein the domain invariant regularization uses a domain invariant loss function that accounts for a content-specific feature comprising subject information or category information and a domain-specific feature comprising specific style information of the input image;

displaying information based on the output; and

wherein the domain invariant machine learning model is trained to be invariant to domain-specific features using training images having disentangled content-specific and domain-specific feature spaces, the training images generated based on a multi-domain image-to-image translation network.

2 . The method of claim 1 , wherein the domain invariant regularization comprises using a domain invariant loss.

3 . The method of claim 1 , wherein the domain invariant machine learning model is trained using a first image of the training images, the first image comprising the content-specific feature and a first domain-specific feature and a second image of the training images, the second image comprising the content-specific feature and a second domain-specific feature.

4 . The method of claim 3 , wherein the second image is generated from the first image.

5 . The method of claim 3 , wherein the domain invariant machine learning model is trained by using a classification loss for the first image, using a domain invariant loss for the second image, and combining the classification loss and the domain invariant loss.

6 . The method of claim 3 , wherein the second image is generated using an image generator.

7 . The method of claim 6 , wherein the image generator is trained using a classifier model.

8 . A system comprising:

one or more processors;

a memory storing instructions which, when executed by the one or more processors, causes a domain invariant machine learning model to compute an output based on an input image, wherein the domain invariant machine learning model is trained using domain invariant regularization comprising a style diversification loss function and a loss function implemented using a distance function, wherein the domain invariant regularization uses a domain invariant loss function that accounts for a content-specific feature comprising subject information or category information and a domain-specific feature comprising specific style information of the input image; and

wherein the domain invariant machine learning model is trained to be invariant to domain-specific features using training images having disentangled content-specific and domain-specific feature spaces, the training images generated based on a multi-domain image-to-image translation network.

9 . The system of claim 8 , wherein the domain invariant regularization comprises using a domain invariant loss.

10 . The system of claim 8 , wherein the domain invariant machine learning model is trained using a first image of the training images, the first image comprising the content-specific feature and a first domain-specific feature and a second image of the training images, the second image comprising the content-specific feature and a second domain-specific feature.

11 . The system of claim 10 , wherein the second image is generated from the first image.

12 . The system of claim 10 , wherein the domain invariant machine learning model is trained by using a classification loss for the first image, using a domain invariant loss for the second image, and combining the classification loss and the domain invariant loss.

13 . The system of claim 10 , wherein the second image is generated using an image generator.

14 . The system of claim 13 , wherein the image generator is trained using a classifier model.

15 . A method of training a machine learning model, the method comprising:

receiving a plurality of input images;

generating a plurality of perturbed input images based on the plurality of input images, wherein the plurality of perturbed input images are generated based on a combination of a content-specific feature comprising subject information or category information and a domain-specific feature comprising specific style information of the plurality of input images;

training the machine learning model to be domain invariant using the plurality of input images, the plurality of perturbed input images, a style diversification loss function, and a loss function implemented using an expectation function; and

where in the machine learning model is a domain invariant machine learning model trained to be invariant to domain-specific features using training images having disentangled content-specific and domain-specific feature spaces, the training images generated based on a multi-domain image-to-image translation network.

16 . The method of claim 15 , wherein training the machine learning model to be domain invariant comprises using a domain invariant loss.

17 . The method of claim 16 , wherein training the machine learning model to be domain invariant comprises:

computing a classification loss for one of the plurality of input images;

computing the domain invariant loss for one of the plurality of perturbed input images; and

combining the classification loss and the domain invariant loss.

18 . The method of claim 15 , wherein one of the plurality of input images comprises the content-specific feature and a first domain-specific feature, and generating the plurality of perturbed input images comprises combining the content-specific feature with a second domain-specific feature.

19 . The method of claim 15 , wherein the plurality of perturbed input images are generated using an image generator.

20 . The method of claim 19 , wherein the image generator is trained using a classifier model.