The last decade has witnessed a surge of interest in applying deep learning models for discovering sequential patterns from a large volume of data. Recent works show that deep learning models can be further improved by enforcing models to learn a smooth output distribution around each data point. This can be achieved by augmenting training data with slight perturbations that are designed to alter model outputs. Such adversarial training approaches have shown much success in improving the generalization performance of deep learning models on static data, e.g., transaction data or image data captured on a single snapshot. However, when applied to sequential data, the standard adversarial training approaches cannot fully capture the discriminative structure of a sequence. This is because real-world sequential data are often collected over a long period of time and may include much irrelevant information to the classification task. To this end, we develop a novel adversarial training approach for sequential data classification by investigating when and how to perturb a sequence for an effective data augmentation. Finally, we demonstrate the superiority of the proposed method over baselines in a diversity of real-world sequential datasets.
Learning Sample-Specific Policies for Sequential Image Augmentation
This paper presents a policy-driven sequential image augmentation
approach for image-related tasks. Our approach applies a sequence
of image transformations (e.g., translation, rotation) over a training
image, one transformation at a time, with the augmented image
from the previous time step treated as the input for the next transformation.
This sequential data augmentation substantially improves
sample diversity, leading to improved test performance, especially
for data-hungry models (e.g., deep neural networks). However, the
search for the optimal transformation of each image at each time
step of the sequence has high complexity due to its combination
nature. To address this challenge, we formulate the search task as a
sequential decision process and introduce a deep policy network
that learns to produce transformations based on image content. We
also develop an iterative algorithm to jointly train a classifier and
the policy network in the reinforcement learning setting. The immediate
reward of a potential transformation is defined to encourage
transformations producing hard samples for the current classifier.
At each iteration, we employ the policy network to augment the
training dataset, train a classifier with the augmented data, and train
the policy net with the aid of the classifier. We apply the above approach
to both public image classification benchmarks and a newly
collected image dataset for material recognition. Comparisons to
alternative augmentation approaches show that our more »
- Award ID(s):
- 1715017
- Publication Date:
- NSF-PAR ID:
- 10300254
- Journal Name:
- MM '21: Proceedings of the 29th ACM International Conference on Multimedia
- Page Range or eLocation-ID:
- 4491 to 4500
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Publisher's Version of Record
- https://doi.org/10.1145/3474085.3475602
