SOTAVerified

Data Augmentation

Data augmentation involves techniques used for increasing the amount of data, based on different modifications, to expand the amount of examples in the original dataset. Data augmentation not only helps to grow the dataset but it also increases the diversity of the dataset. When training machine learning models, data augmentation acts as a regularizer and helps to avoid overfitting.

Data augmentation techniques have been found useful in domains like NLP and computer vision. In computer vision, transformations like cropping, flipping, and rotation are used. In NLP, data augmentation techniques can include swapping, deletion, random insertion, among others.

Further readings:

( Image credit: Albumentations )

Papers

Showing 74017450 of 8378 papers

TitleStatusHype
Improving singing voice separation with the Wave-U-Net using Minimum Hyperspherical EnergyCode0
Using Speech Synthesis to Train End-to-End Spoken Language Understanding ModelsCode2
Boosting Mapping Functionality of Neural Networks via Latent Feature Generation based on Reversible Learning0
MIScnn: A Framework for Medical Image Segmentation with Convolutional Neural Networks and Deep LearningCode0
Cascaded Generation of High-quality Color Visible Face Images from Thermal Captures0
Good, Better, Best: Textual Distractors Generation for Multiple-Choice Visual Question Answering via Reinforcement Learning0
Label-efficient audio classification through multitask learning and self-supervision0
MonaLog: a Lightweight System for Natural Language Inference Based on MonotonicityCode0
Real-Time Lip Sync for Live 2D AnimationCode0
Towards More Sample Efficiency in Reinforcement Learning with Data AugmentationCode0
Illumination-Based Data Augmentation for Robust Background SubtractionCode0
Automatic Data Augmentation by Learning the Deterministic PolicyCode0
Anatomically-Informed Data Augmentation for functional MRI with Applications to Deep Learning0
ODE guided Neural Data Augmentation Techniques for Time Series Data and its Benefits on Robustness0
Self-supervised Label Augmentation via Input TransformationsCode0
Sketch-Specific Data Augmentation for Freehand Sketch Recognition0
Generative Image Translation for Data Augmentation in Colorectal Histopathology ImagesCode0
Cross-Domain Image Classification through Neural-Style Transfer Data AugmentationCode0
Towards DeepSpray: Using Convolutional Neural Network to post-process Shadowgraphy Images of Liquid Atomization0
Adversarial Pulmonary Pathology Translation for Pairwise Chest X-ray Data AugmentationCode0
Network Randomization: A Simple Technique for Generalization in Deep Reinforcement LearningCode0
Efficient and Adaptive Kernelization for Nonlinear Max-margin Multi-view Learning0
First Order Ambisonics Domain Spatial Augmentation for DNN-based Direction of Arrival Estimation0
Unconstrained Road Marking Recognition with Generative Adversarial Networks0
A Closer Look At Feature Space Data Augmentation For Few-Shot Intent Classification0
CONAN -- COunter NArratives through Nichesourcing: a Multilingual Dataset of Responses to Fight Online Hate Speech0
Improving Neural Machine Translation Robustness via Data Augmentation: Beyond Back Translation0
Kornia: an Open Source Differentiable Computer Vision Library for PyTorchCode1
Controlled Text Generation for Data Augmentation in Intelligent Artificial Agents0
Two Stream Networks for Self-Supervised Ego-Motion Estimation0
ANDA: A Novel Data Augmentation Technique Applied to Salient Object DetectionCode0
Partial differential equation regularization for supervised machine learning0
Learning Dense Wide Baseline Stereo Matching for People0
Cardiac Segmentation of LGE MRI with Noisy Labels0
Data Augmentation Based on Distributed Expressions in Text Classification Tasks0
AdaTransform: Adaptive Data Transformation0
Augmenting learning using symmetry in a biologically-inspired domain0
Student Specialization in Deep ReLU Networks With Finite Width and Input Dimension0
On Incorporating Semantic Prior Knowledge in Deep Learning Through Embedding-Space Constraints0
LIP: Learning Instance Propagation for Video Object Segmentation0
RandAugment: Practical automated data augmentation with a reduced search spaceCode2
Lifelong Neural Topic Learning in Contextualized Autoregressive Topic Models of Language via Informative Transfers0
Automatically Learning Data Augmentation Policies for Dialogue TasksCode0
Urban Sound Tagging using Convolutional Neural NetworksCode0
Learning the Difference that Makes a Difference with Counterfactually-Augmented DataCode0
Graph-Preserving Grid Layout: A Simple Graph Drawing Method for Graph Classification using CNNs0
Unsupervised Image Translation using Adversarial Networks for Improved Plant Disease RecognitionCode1
Breast Cancer Diagnosis with Transfer Learning and Global Pooling0
Explainable Deep Learning for Augmentation of sRNA Expression Profiles0
Implicit Semantic Data Augmentation for Deep NetworksCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1DeiT-B (+MixPro)Accuracy (%)82.9Unverified
2ResNet-200 (DeepAA)Accuracy (%)81.32Unverified
3DeiT-S (+MixPro)Accuracy (%)81.3Unverified
4ResNet-200 (Fast AA)Accuracy (%)80.6Unverified
5ResNet-200 (UA)Accuracy (%)80.4Unverified
6ResNet-200 (AA)Accuracy (%)80Unverified
7ResNet-50 (DeepAA)Accuracy (%)78.3Unverified
8ResNet-50 (TA wide)Accuracy (%)78.07Unverified
9ResNet-50 (LoRot-E)Accuracy (%)77.72Unverified
10ResNet-50 (LoRot-I)Accuracy (%)77.71Unverified
#ModelMetricClaimedVerifiedStatus
1WideResNet-40-2 (Faster AA)Percentage error3.7Unverified
2Shake-Shake (26 2×32d) (Faster AA)Percentage error2.7Unverified
3WideResNet-28-10 (Faster AA)Percentage error2.6Unverified
4Shake-Shake (26 2×112d) (Faster AA)Percentage error2Unverified
5Shake-Shake (26 2×96d) (Faster AA)Percentage error2Unverified
#ModelMetricClaimedVerifiedStatus
1DiffAugClassification Accuracy92.7Unverified
2PaCMAPClassification Accuracy85.3Unverified
3hNNEClassification Accuracy77.4Unverified
4TopoAEClassification Accuracy74.6Unverified