Implicit Semantic Data Augmentation for Deep Networks (1909.12220v5)

Abstract: In this paper, we propose a novel implicit semantic data augmentation (ISDA) approach to complement traditional augmentation techniques like flipping, translation or rotation. Our work is motivated by the intriguing property that deep networks are surprisingly good at linearizing features, such that certain directions in the deep feature space correspond to meaningful semantic transformations, e.g., adding sunglasses or changing backgrounds. As a consequence, translating training samples along many semantic directions in the feature space can effectively augment the dataset to improve generalization. To implement this idea effectively and efficiently, we first perform an online estimate of the covariance matrix of deep features for each class, which captures the intra-class semantic variations. Then random vectors are drawn from a zero-mean normal distribution with the estimated covariance to augment the training data in that class. Importantly, instead of augmenting the samples explicitly, we can directly minimize an upper bound of the expected cross-entropy (CE) loss on the augmented training set, leading to a highly efficient algorithm. In fact, we show that the proposed ISDA amounts to minimizing a novel robust CE loss, which adds negligible extra computational cost to a normal training procedure. Although being simple, ISDA consistently improves the generalization performance of popular deep models (ResNets and DenseNets) on a variety of datasets, e.g., CIFAR-10, CIFAR-100 and ImageNet. Code for reproducing our results is available at https://github.com/blackfeather-wang/ISDA-for-Deep-Networks.

• The paper introduces a novel method that leverages online covariance estimation of deep features to perform implicit semantic data augmentation.
• This approach applies random vectors from a Gaussian distribution based on intra-class variations to enhance network generalization.
• Empirical results demonstrate notable performance gains on datasets like CIFAR-10, CIFAR-100, and ImageNet with popular architectures.

Implicit Semantic Data Augmentation for Deep Networks: An Expert Overview

The paper "Implicit Semantic Data Augmentation for Deep Networks" introduces a novel approach to data augmentation, called Implicit Semantic Data Augmentation (ISDA), aiming to enhance the generalization abilities of deep networks. The authors propose a method that leverages latent semantic features within the deep feature space, facilitating efficient data augmentation without the computational overhead associated with auxiliary generative models.

Key Contributions

The ISDA algorithm is grounded in the observation that deep networks can effectively linearize features, revealing directions in the feature space that correspond to semantic transformations. The contribution of this work lies in its ability to conduct implicit semantic transformations by performing an online estimation of intra-class covariance matrices. These matrices capture semantic variations across classes, allowing for efficient data augmentation through random vectors drawn from a normal distribution.

Methodology

The authors describe a process in which the covariance matrix for deep features is computed online, representing intra-class variations. Random vectors, following a zero-mean Gaussian distribution with the estimated covariance, are used for augmentation. Crucially, rather than explicitly generating transformed samples, ISDA minimizes an upper bound of the expected cross-entropy loss of the augmented set, thus sidestepping the computational complexities of generating new samples.

This approach effectively augments data by applying meaningful transformations to the sample features, capturing semantic changes like the addition of sunglasses or background changes. Consequently, ISDA provides improvements in generalization for deep learning models, achieving notable performance enhancements on datasets such as CIFAR-10, CIFAR-100, and ImageNet.

Empirical Results

ISDA exhibits consistent improvements in generalization when applied to popular deep networks such as ResNets and DenseNets. On ImageNet, for example, the Top-1 error rate of ResNet-50 improved from 23.0% to 21.9%. Moreover, ISDA complements existing non-semantic data augmentation techniques like Cutout and AutoAugment, further enhancing model performance.

Theoretical and Practical Implications

On a theoretical level, the development of ISDA provides an alternative perspective on data augmentation, illustrating the power of leveraging semantic transformations within existing feature spaces. Practically, ISDA's integration as a robust loss function makes it highly compatible with existing deep learning architectures, without requiring significant computational resources.

Future Directions

The insights from this paper open several avenues for future work. Expanding ISDA to other domains such as natural language processing or reinforcement learning could yield intriguing results. Additionally, further exploration of the theoretical underpinnings of semantic feature space transformations may lead to new augmentation techniques and improved model architectures.

In summary, the Implicit Semantic Data Augmentation method offers an innovative way to enhance deep network training efficiency and generalization, making it a valuable contribution to the field of deep learning.