Systems and methods are disclosed for training a learning machine by augmenting data from fine-grained image recognition with labeled data annotated by one or more hyper-classes, performing multi-task deep learning; allowing fine-grained classification and hyper-class classification to share and learn the same feature layers; and applying regularization in the multi-task deep learning to exploit one or more relationships between the fine-grained classes and the hyper-classes.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for training a learning machine, comprising: augmenting data from fine-grained image recognition with labeled data annotated by one or more hyper-classes, performing a multi-task deep learning on the labeled data; allowing fine-grained classification and hyper-class classification to share and learn the same feature layers; and applying regularization in the multi-task deep learning to exploit one or more relationships between the fine-grained classes and the hyper-classes. 2. The method of claim 1, comprising two common hyper-classes with one being a super-classes that subsume a set of fine-grained classes and another being named factor-classes on different viewpoints of a car that explain the large intra-class variance. 3. The method of claim 1, comprising identifying annotated hyper-classes in the fine-grained data and acquiring a large number of hyper-classes labeled images from external sources. 4. The method of claim 3, wherein the external sources include image search engines. 5. The method of claim 1, comprising applying a learning model engine from a regularization between the fine-grained recognition and the hyper-class recognition. 6. The method of claim 1, comprising performing data augmentation to utilize auxiliary images as to improve a generalization performance of learned features. 7. The method of claim 1, comprising applying a hyper-class to capture ‘has a’ relationship. 8. The method of claim 7, comprising applying the hyper-class to explain intra-class variances or pose variance. 9. The method of claim 1, comprising solving: min { w v , c } , { u v } , { w l }   L  ( { w v , c } , { u v } ) + R  ( { w v , c } , { u v } ) + ∑ v = 1 K   r  ( u v ) + ∑ l = 1 H   r  ( w l ) where wl, l=1, . . . , H denotes all the weights of the CNN in determining the high level features h(x), H denotes the number of layers before the classifier layers, and r(w) denotes the standard Euclidean norm square regularizer with an implicit regularization parameter (or a weight decay parameter). 10. The method of claim 1, comprising training the deep CNN by backpropagation using a mini-batch stochastic gradient descent with two sources of data and two loss functions corresponding to the tasks, further comprising sampling images in a mini-batch to determine stochastic gradients. 11. A learning system, comprising: low level feature extractors; high level feature extractors coupled to the low level feature extractors; and a plurality of classifiers receiving high and low level features, with a softmax loss on auxiliary data and softmax loss on fine-grained data, the classifiers forming a hyper-class augmented and regularized deep Convolution Neural Network (CNN). 12. The system of claim 11, comprising two common hyper-classes with one being a super-classes that subsume a set of fine-grained classes and another being named factor-classes on different viewpoints of a car that explain the large intra-class variance. 13. The system of claim 11, comprising annotated hyper-classes from in fine-grained data and acquiring hyper-classes labeled images from external sources. 14. The system of claim 13, comprising, wherein the external sources include image search engines. 15. The system of claim 11, comprising a learning model engine derived from a regularization between a fine-grained recognition and a hyper-class recognition. 16. The system of claim 11, wherein data augmentation is used to utilize auxiliary images as to improve a generalization performance of learned features. 17. The system of claim 11, comprising applying a hyper-class to capture ‘has a’ relationship. 18. The system of claim 17, wherein the hyper-class is used to explain intra-class variances or pose variance. 19. The system of claim 11, comprising code to determine: min { w v , c } , { u v } , { w l }   L  ( { w v , c } , { u v } ) + R  ( { w v , c } , { u v } ) + ∑ v = 1 K   r  ( u v ) + ∑ l = 1 H   r  ( w l ) where wl, l=1, . . . , H denotes all the weights of the CNN in determining the high level features h(x) denotes the number of layers before the classifier layers, and r(w) denotes the standard Euclidean norm square regularizer with an implicit regularization parameter (or a weight decay parameter). 20. The system of claim 11, wherein the deep CNN is trained by backpropagation using a mini-batch stochastic gradient descent with two sources of data and two loss functions corresponding to the tasks, further comprising code for sampling images in a mini-batch to determine stochastic gradients.