6 research outputs found
An overview of mixing augmentation methods and augmentation strategies
Deep Convolutional Neural Networks have made an incredible progress in many
Computer Vision tasks. This progress, however, often relies on the availability
of large amounts of the training data, required to prevent over-fitting, which
in many domains entails significant cost of manual data labeling. An
alternative approach is application of data augmentation (DA) techniques that
aim at model regularization by creating additional observations from the
available ones. This survey focuses on two DA research streams: image mixing
and automated selection of augmentation strategies. First, the presented
methods are briefly described, and then qualitatively compared with respect to
their key characteristics. Various quantitative comparisons are also included
based on the results reported in recent DA literature. This review mainly
covers the methods published in the materials of top-tier conferences and in
leading journals in the years 2017-2021
AttentionMix: Data augmentation method that relies on BERT attention mechanism
The Mixup method has proven to be a powerful data augmentation technique in
Computer Vision, with many successors that perform image mixing in a guided
manner. One of the interesting research directions is transferring the
underlying Mixup idea to other domains, e.g. Natural Language Processing (NLP).
Even though there already exist several methods that apply Mixup to textual
data, there is still room for new, improved approaches. In this work, we
introduce AttentionMix, a novel mixing method that relies on attention-based
information. While the paper focuses on the BERT attention mechanism, the
proposed approach can be applied to generally any attention-based model.
AttentionMix is evaluated on 3 standard sentiment classification datasets and
in all three cases outperforms two benchmark approaches that utilize Mixup
mechanism, as well as the vanilla BERT method. The results confirm that the
attention-based information can be effectively used for data augmentation in
the NLP domain
Training CNN classifiers solely on webly data
Real life applications of deep learning (DL) are often limited by the lack of expert labeled data required to effectively train DL models. Creation of such data usually requires substantial amount of time for manual categorization, which is costly and is considered to be one of the major impediments in development of DL methods in many areas. This work proposes a classification approach which completely removes the need for costly expert labeled data and utilizes noisy web data created by the users who are not subject matter experts. The experiments are performed with two well-known Convolutional Neural Network (CNN) architectures: VGG16 and ResNet50 trained on three randomly collected Instagram-based sets of images from three distinct domains: metropolitan cities, popular food and common objects - the last two sets were compiled by the authors and made freely available to the research community. The dataset containing common objects is a webly counterpart of PascalVOC2007 set. It is demonstrated that despite significant amount of label noise in the training data, application of proposed approach paired with standard training CNN protocol leads to high classification accuracy on representative data in all three above-mentioned domains. Additionally, two straightforward procedures of automatic cleaning of the data, before its use in the training process, are proposed. Apparently, data cleaning does not lead to improvement of results which suggests that the presence of noise in webly data is actually helpful in learning meaningful and robust class representations. Manual inspection of a subset of web-based test data shows that labels assigned to many images are ambiguous even for humans. It is our conclusion that for the datasets and CNN architectures used in this paper, in case of training with webly data, a major factor contributing to the final classification accuracy is representativeness of test data rather than application of data cleaning procedures