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Data Augmentation

45 directly classified papers

Papers per year

Papers

BulletTrain: Accelerating Robust Neural Network Training via Boundary Example Mining NIPS 2021
Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style NIPS 2021
StyleMix: Separating Content and Style for Enhanced Data Augmentation CVPR 2021
The UniMelb Submission to the SIGMORPHON 2020 Shared Task 0: Typologically Diverse Morphological Inflection ACL 2020
Partial Label Learning with Batch Label Correction AAAI 2020
Augment Your Batch: Improving Generalization Through Instance Repetition CVPR 2020
BachGAN: High-Resolution Image Synthesis From Salient Object Layout CVPR 2020
SpecSwap: A Simple Data Augmentation Method for End-to-End Speech Recognition INTERSPEECH 2020
Leveraging BERT with Mixup for Sentence Classification (Student Abstract) AAAI 2020
MixText: Linguistically-Informed Interpolation of Hidden Space for Semi-Supervised Text Classification ACL 2020
Ensemble Self-Training for Low-Resource Languages: Grapheme-to-Phoneme Conversion and Morphological Inflection ACL 2020
DeepCCFV: Camera Constraint-Free Multi-View Convolutional Neural Network for 3D Object Retrieval AAAI 2019
On Mixup Training: Improved Calibration and Predictive Uncertainty for Deep Neural Networks NIPS 2019
Bag of Tricks for Image Classification with Convolutional Neural Networks CVPR 2019
PROMT Systems for WMT 2019 Shared Translation Task ACL 2019
Adversarially Occluded Samples for Person Re-Identification CVPR 2018
Classification-Driven Dynamic Image Enhancement CVPR 2018
Active Bias: Training More Accurate Neural Networks by Emphasizing High Variance Samples NIPS 2017
Reflectance Adaptive Filtering Improves Intrinsic Image Estimation CVPR 2017
PatchBatch: A Batch Augmented Loss for Optical Flow CVPR 2016