52,038 research outputs found
PanDA: Panoptic Data Augmentation
The recently proposed panoptic segmentation task presents a significant challenge of image understanding with computer vision by unifying semantic segmentation and instance segmentation tasks. In this paper we present an efficient and novel panoptic data augmentation (PanDA) method which operates exclusively in pixel space, requires no additional data or training, and is computationally cheap to implement. By retraining original state-of-the-art models on PanDA augmented datasets generated with a single frozen set of parameters, we show robust performance gains in panoptic segmentation, instance segmentation, as well as detection across models, backbones, dataset domains, and scales. Finally, the effectiveness of unrealistic-looking training images synthesized by PanDA suggest that one should rethink the need for image realism for efficient data augmentation
Counterexample-Guided Data Augmentation
We present a novel framework for augmenting data sets for machine learning
based on counterexamples. Counterexamples are misclassified examples that have
important properties for retraining and improving the model. Key components of
our framework include a counterexample generator, which produces data items
that are misclassified by the model and error tables, a novel data structure
that stores information pertaining to misclassifications. Error tables can be
used to explain the model's vulnerabilities and are used to efficiently
generate counterexamples for augmentation. We show the efficacy of the proposed
framework by comparing it to classical augmentation techniques on a case study
of object detection in autonomous driving based on deep neural networks
Scalable Data Augmentation for Deep Learning
Scalable Data Augmentation (SDA) provides a framework for training deep
learning models using auxiliary hidden layers. Scalable MCMC is available for
network training and inference. SDA provides a number of computational
advantages over traditional algorithms, such as avoiding backtracking, local
modes and can perform optimization with stochastic gradient descent (SGD) in
TensorFlow. Standard deep neural networks with logit, ReLU and SVM activation
functions are straightforward to implement. To illustrate our architectures and
methodology, we use P\'{o}lya-Gamma logit data augmentation for a number of
standard datasets. Finally, we conclude with directions for future research
Data Augmentation for Skin Lesion Analysis
Deep learning models show remarkable results in automated skin lesion
analysis. However, these models demand considerable amounts of data, while the
availability of annotated skin lesion images is often limited. Data
augmentation can expand the training dataset by transforming input images. In
this work, we investigate the impact of 13 data augmentation scenarios for
melanoma classification trained on three CNNs (Inception-v4, ResNet, and
DenseNet). Scenarios include traditional color and geometric transforms, and
more unusual augmentations such as elastic transforms, random erasing and a
novel augmentation that mixes different lesions. We also explore the use of
data augmentation at test-time and the impact of data augmentation on various
dataset sizes. Our results confirm the importance of data augmentation in both
training and testing and show that it can lead to more performance gains than
obtaining new images. The best scenario results in an AUC of 0.882 for melanoma
classification without using external data, outperforming the top-ranked
submission (0.874) for the ISIC Challenge 2017, which was trained with
additional data.Comment: 8 pages, 3 figures, to be presented on ISIC Skin Image Analysis
Worksho
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