A Kernel Theory of Modern Data Augmentation

Tri Dao, Albert Gu, Alexander Ratner, Virginia Smith, Chris De Sa, Christopher Re
Proceedings of the 36th International Conference on Machine Learning, PMLR 97:1528-1537, 2019.

Abstract

Data augmentation, a technique in which a training set is expanded with class-preserving transformations, is ubiquitous in modern machine learning pipelines. In this paper, we seek to establish a theoretical framework for understanding data augmentation. We approach this from two directions: First, we provide a general model of augmentation as a Markov process, and show that kernels appear naturally with respect to this model, even when we do not employ kernel classification. Next, we analyze more directly the effect of augmentation on kernel classifiers, showing that data augmentation can be approximated by first-order feature averaging and second-order variance regularization components. These frameworks both serve to illustrate the ways in which data augmentation affects the downstream learning model, and the resulting analyses provide novel connections between prior work in invariant kernels, tangent propagation, and robust optimization. Finally, we provide several proof-of-concept applications showing that our theory can be useful for accelerating machine learning workflows, such as reducing the amount of computation needed to train using augmented data, and predicting the utility of a transformation prior to training.

Cite this Paper


BibTeX
@InProceedings{pmlr-v97-dao19b, title = {A Kernel Theory of Modern Data Augmentation}, author = {Dao, Tri and Gu, Albert and Ratner, Alexander and Smith, Virginia and De Sa, Chris and Re, Christopher}, booktitle = {Proceedings of the 36th International Conference on Machine Learning}, pages = {1528--1537}, year = {2019}, editor = {Chaudhuri, Kamalika and Salakhutdinov, Ruslan}, volume = {97}, series = {Proceedings of Machine Learning Research}, month = {09--15 Jun}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v97/dao19b/dao19b.pdf}, url = {http://proceedings.mlr.press/v97/dao19b.html}, abstract = {Data augmentation, a technique in which a training set is expanded with class-preserving transformations, is ubiquitous in modern machine learning pipelines. In this paper, we seek to establish a theoretical framework for understanding data augmentation. We approach this from two directions: First, we provide a general model of augmentation as a Markov process, and show that kernels appear naturally with respect to this model, even when we do not employ kernel classification. Next, we analyze more directly the effect of augmentation on kernel classifiers, showing that data augmentation can be approximated by first-order feature averaging and second-order variance regularization components. These frameworks both serve to illustrate the ways in which data augmentation affects the downstream learning model, and the resulting analyses provide novel connections between prior work in invariant kernels, tangent propagation, and robust optimization. Finally, we provide several proof-of-concept applications showing that our theory can be useful for accelerating machine learning workflows, such as reducing the amount of computation needed to train using augmented data, and predicting the utility of a transformation prior to training.} }
Endnote
%0 Conference Paper %T A Kernel Theory of Modern Data Augmentation %A Tri Dao %A Albert Gu %A Alexander Ratner %A Virginia Smith %A Chris De Sa %A Christopher Re %B Proceedings of the 36th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2019 %E Kamalika Chaudhuri %E Ruslan Salakhutdinov %F pmlr-v97-dao19b %I PMLR %P 1528--1537 %U http://proceedings.mlr.press/v97/dao19b.html %V 97 %X Data augmentation, a technique in which a training set is expanded with class-preserving transformations, is ubiquitous in modern machine learning pipelines. In this paper, we seek to establish a theoretical framework for understanding data augmentation. We approach this from two directions: First, we provide a general model of augmentation as a Markov process, and show that kernels appear naturally with respect to this model, even when we do not employ kernel classification. Next, we analyze more directly the effect of augmentation on kernel classifiers, showing that data augmentation can be approximated by first-order feature averaging and second-order variance regularization components. These frameworks both serve to illustrate the ways in which data augmentation affects the downstream learning model, and the resulting analyses provide novel connections between prior work in invariant kernels, tangent propagation, and robust optimization. Finally, we provide several proof-of-concept applications showing that our theory can be useful for accelerating machine learning workflows, such as reducing the amount of computation needed to train using augmented data, and predicting the utility of a transformation prior to training.
APA
Dao, T., Gu, A., Ratner, A., Smith, V., De Sa, C. & Re, C.. (2019). A Kernel Theory of Modern Data Augmentation. Proceedings of the 36th International Conference on Machine Learning, in Proceedings of Machine Learning Research 97:1528-1537 Available from http://proceedings.mlr.press/v97/dao19b.html.

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