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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1701–1725 of 8378 papers

TitleStatusHype
A Data-Centric Approach for Improving Adversarial Training Through the Lens of Out-of-Distribution Detection—0
Data Augmentation in Earth Observation: A Diffusion Model Approach—0
A Lottery Ticket Hypothesis Framework for Low-Complexity Device-Robust Neural Acoustic Scene Classification—0
Augmentation Learning for Semi-Supervised Classification—0
Accenture at CheckThat! 2021: Interesting claim identification and ranking with contextually sensitive lexical training data augmentation—0
Augmentation Invariant Manifold Learning—0
Augmentation Inside the Network—0
3D CNN-based classification using sMRI and MD-DTI images for Alzheimer disease studies—0
Data Augmentation in Emotion Classification Using Generative Adversarial Networks—0
Augmentation-induced Consistency Regularization for Classification—0
A Localisation-Segmentation Approach for Multi-label Annotation of Lumbar Vertebrae using Deep Nets—0
Augmentation-based Unsupervised Cross-Domain Functional MRI Adaptation for Major Depressive Disorder Identification—0
All-Weather Object Recognition Using Radar and Infrared Sensing—0
A data augmentation strategy for deep neural networks with application to epidemic modelling—0
Augmentation-Based Deep Learning for Identification of Circulating Tumor Cells—0
A Data Augmentation Pipeline to Generate Synthetic Labeled Datasets of 3D Echocardiography Images using a GAN—0
Trainable Pointwise Decoder Module for Point Cloud Segmentation—0
A limited-size ensemble of homogeneous CNN/LSTMs for high-performance word classification—0
Augment-and-Conquer Negative Binomial Processes—0
Accent conversion using discrete units with parallel data synthesized from controllable accented TTS—0
Data Augmentation Imbalance For Imbalanced Attribute Classification—0
Data Augmentation in Graph Neural Networks: The Role of Generated Synthetic Graphs—0
AlignMix: Improving representations by interpolating aligned features—0
AugLoss: A Robust Augmentation-based Fine Tuning Methodology—0
Image retrieval outperforms diffusion models on data augmentation—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