DOPING: Generative Data Augmentation for Unsupervised Anomaly Detection with GAN
, 2018 Recently, the introduction of the generative adversarial network (GAN) and its variants has enabled the generation of realistic synthetic samples, which has been used for enlarging training sets.
Cheung, Ngai-Man +5 more
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How to choose “Good” Samples for Text Data Augmentation
, 2022 Abstract Deep learning-based text classification models need abundant labeled data to obtain competitive performance. Unfortunately, annotating large-size corpus is time-consuming and laborious. To tackle this, multiple researches try to use data augmentation to expand the corpus size.
Lin, Xiaotian +4 more
openaire +2 more sources
On the Generalization Effects of Linear Transformations in Data Augmentation
, 2020 Data augmentation is a powerful technique to improve performance in applications such as image and text classification tasks. Yet, there is little rigorous understanding of why and how various augmentations work.
Ré, Christopher +3 more
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ACGAN-based Data Augmentation Integrated with Long-term Scalogram for Acoustic Scene Classification
, 2020 In acoustic scene classification (ASC), acoustic features play a crucial role in the extraction of scene information, which can be stored over different time scales.
Chen, Hangting +3 more
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Bayesian Robust Inference of Sample Selection Using Selection-t Models
, 2014 Heckman selection model is the most popular econometric model in analysis of data with sample selection. However, selection models with Normal errors cannot accommodate heavy tails in the error distribution.
Ding, Peng
core +1 more source
Modeling Camera Effects to Improve Visual Learning from Synthetic Data
, 2018 Recent work has focused on generating synthetic imagery to increase the size and variability of training data for learning visual tasks in urban scenes.
A Andreopoulos +10 more
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Image Data Augmentation for SAR Sensor via Generative Adversarial Nets
IEEE Access, 2019 As a mission-critical sensor, SAR has been applied in environmental monitoring and battlefield surveillance; moreover, SAR target recognition is one of the most important applications of SAR technology.
Zongyong Cui +3 more
doaj +1 more source
How to Fine-tune Models with Few Samples: Update, Data Augmentation, and Test-time Augmentation
, 2022 Most of the recent few-shot learning (FSL) algorithms are based on transfer learning, where a model is pre-trained using a large amount of source data, and the pre-trained model is fine-tuned using a small amount of target data. In transfer learning-based FSL, sophisticated pre-training methods have been widely studied for universal representation ...
Kim, Yujin +3 more
openaire +2 more sources
The Effect of Biologic Materials and Oral Steroids on Radiographic and Clinical Outcomes of Horizontal Alveolar Ridge Augmentation. [PDF]
, 2018 The purpose of this study was to investigate if the addition of biologic materials and/or oral steroids would affect horizontal bone gain, or the bone density of the grafted bone in horizontal alveolar ridge augmentations.
Reichert, Amy
core +1 more source
Bayesian Sampling Algorithms for the Sample Selection and Two-Part Models [PDF]
This paper considers two models to deal with an outcome variable that contains a large fraction of zeros, such as individual expenditures on health care: a sample-selection model and a two-part model.
Martijn van Hasselt
core