2 research outputs found
Adversarial Learning of General Transformations for Data Augmentation
Data augmentation (DA) is fundamental against overfitting in large
convolutional neural networks, especially with a limited training dataset. In
images, DA is usually based on heuristic transformations, like geometric or
color transformations. Instead of using predefined transformations, our work
learns data augmentation directly from the training data by learning to
transform images with an encoder-decoder architecture combined with a spatial
transformer network. The transformed images still belong to the same class but
are new, more complex samples for the classifier. Our experiments show that our
approach is better than previous generative data augmentation methods, and
comparable to predefined transformation methods when training an image
classifier
Model Patching: Closing the Subgroup Performance Gap with Data Augmentation
Classifiers in machine learning are often brittle when deployed. Particularly
concerning are models with inconsistent performance on specific subgroups of a
class, e.g., exhibiting disparities in skin cancer classification in the
presence or absence of a spurious bandage. To mitigate these performance
differences, we introduce model patching, a two-stage framework for improving
robustness that encourages the model to be invariant to subgroup differences,
and focus on class information shared by subgroups. Model patching first models
subgroup features within a class and learns semantic transformations between
them, and then trains a classifier with data augmentations that deliberately
manipulate subgroup features. We instantiate model patching with CAMEL, which
(1) uses a CycleGAN to learn the intra-class, inter-subgroup augmentations, and
(2) balances subgroup performance using a theoretically-motivated subgroup
consistency regularizer, accompanied by a new robust objective. We demonstrate
CAMEL's effectiveness on 3 benchmark datasets, with reductions in robust error
of up to 33% relative to the best baseline. Lastly, CAMEL successfully patches
a model that fails due to spurious features on a real-world skin cancer
dataset