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

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Showing 7501–7525 of 8378 papers

TitleStatusHype
Data Augmentation Based on Distributed Expressions in Text Classification Tasks—0
AdaTransform: Adaptive Data Transformation—0
Student Specialization in Deep ReLU Networks With Finite Width and Input Dimension—0
On Incorporating Semantic Prior Knowledge in Deep Learning Through Embedding-Space Constraints—0
LIP: Learning Instance Propagation for Video Object Segmentation—0
Lifelong Neural Topic Learning in Contextualized Autoregressive Topic Models of Language via Informative Transfers—0
Urban Sound Tagging using Convolutional Neural NetworksCode0
Automatically Learning Data Augmentation Policies for Dialogue TasksCode0
Explainable Deep Learning for Augmentation of sRNA Expression Profiles—0
Classification of Histopathological Biopsy Images Using Ensemble of Deep Learning Networks—0
Graph-Preserving Grid Layout: A Simple Graph Drawing Method for Graph Classification using CNNs—0
Learning the Difference that Makes a Difference with Counterfactually-Augmented DataCode0
A Hybrid Deep Learning Architecture for Leukemic B-lymphoblast Classification—0
Breast Cancer Diagnosis with Transfer Learning and Global Pooling—0
Breaking the Data Barrier: Towards Robust Speech Translation via Adversarial Stability Training—0
Regularising Deep Networks with Deep Generative Models—0
Speech Recognition with Augmented Synthesized Speech—0
When Covariate-shifted Data Augmentation Increases Test Error And How to Fix It—0
Resizable Neural Networks—0
Data Augmentation in Training CNNs: Injecting Noise to Images—0
Adaptive Data Augmentation with Deep Parallel Generative Models—0
Atalaya at TASS 2019: Data Augmentation and Robust Embeddings for Sentiment Analysis—0
Locally adaptive activation functions with slope recovery term for deep and physics-informed neural networks—0
On Incorporating Semantic Prior Knowlegde in Deep Learning Through Embedding-Space Constraints—0
Invariant Transform Experience Replay: Data Augmentation for Deep Reinforcement LearningCode0
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All datasetsImageNetCIFAR-10GA1457

Benchmark Results

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