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:

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Papers

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Showing 4926–4950 of 8378 papers

TitleStatusHype
Understanding and Mitigating the Bias Inheritance in LLM-based Data Augmentation on Downstream Tasks—0
Understanding Augmentation-based Self-Supervised Representation Learning via RKHS Approximation and Regression—0
On the Calibration of Multilingual Question Answering LLMs—0
Understanding Community Bias Amplification in Graph Representation Learning—0
Understanding data augmentation for classification: when to warp?—0
Understanding Data Augmentation from a Robustness Perspective—0
Understanding Data Augmentation in Neural Machine Translation: Two Perspectives towards Generalization—0
PAC-Chernoff Bounds: Understanding Generalization in the Interpolation Regime—0
Understanding Learning Invariance in Deep Linear Networks—0
Understanding Masked Autoencoders From a Local Contrastive Perspective—0
Understanding Overfitting in Adversarial Training via Kernel Regression—0
Understanding Overfitting in Reweighting Algorithms for Worst-group Performance—0
Understanding Robustness in Teacher-Student Setting: A New Perspective—0
Understanding Social Support Needs in Questions: A Hybrid Approach Integrating Semi-Supervised Learning and LLM-based Data Augmentation—0
Understanding tables with intermediate pre-training—0
Understanding Test-Time Augmentation—0
Understanding the Challenges When 3D Semantic Segmentation Faces Class Imbalanced and OOD Data—0
Understanding Diffusion Objectives as the ELBO with Simple Data Augmentation—0
Understanding the Effect of Data Augmentation on Knowledge Distillation—0
Understanding the Generalization Gap in Visual Reinforcement Learning—0
Understanding the Success of Knowledge Distillation -- A Data Augmentation Perspective—0
Understanding Unconventional Preprocessors in Deep Convolutional Neural Networks for Face Identification—0
Underwater Acoustic Target Recognition based on Smoothness-inducing Regularization and Spectrogram-based Data Augmentation—0
UNet++ and LSTM combined approach for Breast Ultrasound Image Segmentation—0
UnICLAM:Contrastive Representation Learning with Adversarial Masking for Unified and Interpretable Medical Vision Question Answering—0
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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