ArticleAdvances in neural information processing systems2017
Learning to Compose Domain-Specific Transformations for Data Augmentation.
Article in Advances in neural information processing systems, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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Who cites it
34 citing papers in PubMed, 98 citations in OpenAlex.
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- Development of a cerebellar ataxia diagnosis model using conditional GAN-based synthetic data generation for visuomotor adaptation task.BMC medical informatics and decision making · 2024Article
- A Metric-Based Few-Shot Learning Method for Fish Species Identification with Limited Samples.Animals : an open access journal from MDPI · 2024Article
- Augmented drug combination dataset to improve the performance of machine learning models predicting synergistic anticancer effects.Scientific reports · 2024Article
- Semantic-aware Video Representation for Few-shot Action Recognition.IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision · 2024Article
- Brain-inspired semantic data augmentation for multi-style images.Frontiers in neurorobotics · 2024Article
- Augmented drug combination dataset to improve the performance of machine learning models predicting synergistic anticancer effects.Research square · 2023Article
- Regularization for Unsupervised Learning of Optical Flow.Sensors (Basel, Switzerland) · 2023Article
- Evaluating semi-supervision methods for medical image segmentation: applications in cardiac magnetic resonance imaging.Journal of medical imaging (Bellingham, Wash.) · 2023Article
- Artificial intelligence applications in pediatric oncology diagnosis.Exploration of targeted anti-tumor therapy · 2023Review
- Detecting motor symptom fluctuations in Parkinson's disease with generative adversarial networks.NPJ digital medicine · 2022Article
- A Pragmatic Machine Learning Approach to Quantify Tumor-Infiltrating Lymphocytes in Whole Slide Images.Cancers · 2022Article
- Semi-Automatic Prostate Segmentation From Ultrasound Images Using Machine Learning and Principal Curve Based on Interpretable Mathematical Model Expression.Frontiers in oncology · 2022Article
- Optimizing Few-Shot Learning Based on Variational Autoencoders.Entropy (Basel, Switzerland) · 2021Article
- A Bottom-Up Review of Image Analysis Methods for Suspicious Region Detection in Mammograms.Journal of imaging · 2021Review
- Training deep-learning segmentation models from severely limited data.Medical physics · 2021Article
- Selective synthetic augmentation with HistoGAN for improved histopathology image classification.Medical image analysis · 2021Article
- Short Circuit Recognition for Metal Electrorefining Using an Improved Faster R-CNN With Synthetic Infrared Images.Frontiers in neurorobotics · 2021Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
Data augmentation is a ubiquitous technique for increasing the size of labeled training sets by leveraging task-specific data transformations that preserve class labels. While it is often easy for domain experts to specify individual transformations, constructing and tuning the more sophisticated compositions typically needed to achieve state-of-the-art results is a time-consuming manual task in practice. We propose a method for automating this process by learning a generative sequence model over user-specified transformation functions using a generative adversarial approach. Our method can make use of arbitrary, non-deterministic transformation functions, is robust to misspecified user input, and is trained on unlabeled data. The learned transformation model can then be used to perform data augmentation for any end discriminative model. In our experiments, we show the efficacy of our approach on both image and text datasets, achieving improvements of 4.0 accuracy points on CIFAR-10, 1.4 F1 points on the ACE relation extraction task, and 3.4 accuracy points when using domain-specific transformation operations on a medical imaging dataset as compared to standard heuristic augmentation approaches.
Identifiers
29375240PMC5786274W2963543962What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.