Dataset Augmentation in Feature Space (1702.05538v1)

Abstract: Dataset augmentation, the practice of applying a wide array of domain-specific transformations to synthetically expand a training set, is a standard tool in supervised learning. While effective in tasks such as visual recognition, the set of transformations must be carefully designed, implemented, and tested for every new domain, limiting its re-use and generality. In this paper, we adopt a simpler, domain-agnostic approach to dataset augmentation. We start with existing data points and apply simple transformations such as adding noise, interpolating, or extrapolating between them. Our main insight is to perform the transformation not in input space, but in a learned feature space. A re-kindling of interest in unsupervised representation learning makes this technique timely and more effective. It is a simple proposal, but to-date one that has not been tested empirically. Working in the space of context vectors generated by sequence-to-sequence models, we demonstrate a technique that is effective for both static and sequential data.

• The paper presents a novel framework that augments datasets in a learned feature space using noise, interpolation, and extrapolation.
• It leverages a sequence autoencoder to transform raw inputs into context vectors, enabling more meaningful data diversity.
• Experimental results across speech, sensor, and image data demonstrate that feature space extrapolation significantly improves model performance.

Dataset Augmentation in Feature Space: An Expert Overview

The paper by Terrance DeVries and Graham W. Taylor presents a novel approach to dataset augmentation within a learned feature space, diverging from traditional domain-specific augmentation strategies. The methodology is predicated on the idea that meaningful transformations in learned feature spaces can be achieved through simple operations such as noise addition, interpolation, and extrapolation on context vectors derived from sequence-to-sequence models. The proposed approach attempts to address limitations in current dataset augmentation techniques that require careful domain-specific transformation design and testing.

Key Components of the Research

The authors introduce a framework where data augmentation occurs in a latent, learned feature space as opposed to the raw input space. This feature space is constructed using sequence autoencoders (SA), allowing the technique to be applicable to both static and sequential data. By operating in the feature space, the approach aims to leverage the manifold hypothesis, where higher-order representations allow for more meaningful data augmentation due to the expanded relative volume of plausible data points, thus increasing the realism of generated samples.

Augmentation Techniques

1. Noise Addition: Random noise is added to the context vectors. Although straightforward, the effectiveness of this method is dataset-dependent, as excessive noise might lead to the generation of examples that no longer belong to the same class.
2. Interpolation: This involves creating synthetic data points between context vectors of the same class using simple linear interpolation. However, the results indicate that interpolation alone could lead to less variability, thus not always improving model performance.
3. Extrapolation: Distinguished as the most effective strategy, extrapolation involves generating new samples that extend beyond the existing dataset boundaries in feature space, mimicking underrepresented cases and thereby enhancing model robustness.

Experimental Evaluation

The paper rigorously applies the proposed augmentation strategies across multiple domains, including speech recognition (Arabic Digits), sensor data (AUSLAN, UCFKinect), and image classification (MNIST, CIFAR-10).

• For Arabic Digits, extrapolation reduced the baseline model’s error rate significantly, underscoring its potential to enhance model performance when the dataset’s class boundaries are complex.
• AUSLAN and UCFKinect datasets benefited from feature space extrapolation, showcasing improvements in classification accuracy, and aligning with state-of-the-art results.
• In image classification tasks, such as MNIST and CIFAR-10, feature space extrapolation showed promise, sometimes outperforming traditional image space augmentation techniques, particularly when complemented with domain-specific augmentations.

Implications and Future Directions

The implications of this research are profound: it suggests a universal, domain-agnostic data augmentation technique that enhances dataset diversity without needing handcrafted transformations. As unsupervised learning and feature representation advance, this method may seamlessly integrate into existing pipelines across various domains.

However, challenges remain in understanding the sensitivity of this method to different datasets and model architectures. Future research could explore optimizing interpolation and extrapolation parameters, exploring adaptive augmentation strategies that respond to dataset characteristics, and extending the approach to more complex generative models.

In conclusion, this paper contributes a timely and pragmatic solution to dataset augmentation challenges, marking a significant step toward general-purpose data augmentation. While not entirely replacing domain-specific methods, it provides a robust complementary technique, especially useful in domains with scarce labeled data. The exploration of feature space for augmentation opens new avenues for enhancing the generalization capabilities of machine learning models, potentially impacting a wide array of applications.