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Data Augmentation

434 directly classified papers

Papers per year

Papers

Robustness Testing of Language Understanding in Task-Oriented Dialog ACL 2021
Changes in European Solidarity Before and During COVID-19: Evidence from a Large Crowd- and Expert-Annotated Twitter Dataset ACL 2021
What Ingredients Make for an Effective Crowdsourcing Protocol for Difficult NLU Data Collection Tasks? ACL 2021
Can vectors read minds better than experts? Comparing data augmentation strategies for the automated scoring of children’s mindreading ability ACL 2021
AugNLG: Few-shot Natural Language Generation using Self-trained Data Augmentation ACL 2021
Exploring the Efficacy of Automatically Generated Counterfactuals for Sentiment Analysis ACL 2021
ReMix: Towards Image-to-Image Translation With Limited Data CVPR 2021
SuperMix: Supervising the Mixing Data Augmentation CVPR 2021
Robust and Accurate Object Detection via Adversarial Learning CVPR 2021
Recorrupted-to-Recorrupted: Unsupervised Deep Learning for Image Denoising CVPR 2021
Simple Copy-Paste Is a Strong Data Augmentation Method for Instance Segmentation CVPR 2021
Repopulating Street Scenes CVPR 2021
KeepAugment: A Simple Information-Preserving Data Augmentation Approach CVPR 2021
Privacy-Preserving Collaborative Learning With Automatic Transformation Search CVPR 2021
Regularization Strategy for Point Cloud via Rigidly Mixed Sample CVPR 2021
LiDAR-Aug: A General Rendering-Based Augmentation Framework for 3D Object Detection CVPR 2021
GeoSim: Realistic Video Simulation via Geometry-Aware Composition for Self-Driving CVPR 2021
PoseAug: A Differentiable Pose Augmentation Framework for 3D Human Pose Estimation CVPR 2021
On Feature Normalization and Data Augmentation CVPR 2021
RSG: A Simple but Effective Module for Learning Imbalanced Datasets CVPR 2021
AutoFlow: Learning a Better Training Set for Optical Flow CVPR 2021
Improving Panoptic Segmentation at All Scales CVPR 2021
Improved Handling of Motion Blur in Online Object Detection CVPR 2021
StyleMix: Separating Content and Style for Enhanced Data Augmentation CVPR 2021
AutoDO: Robust AutoAugment for Biased Data With Label Noise via Scalable Probabilistic Implicit Differentiation CVPR 2021