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.

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Papers

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Showing 8251–8275 of 8378 papers

TitleStatusHype
Locally Adaptive Dynamic Networks—0
Local Magnification for Data and Feature Augmentation—0
Local Region Perception and Relationship Learning Combined with Feature Fusion for Facial Action Unit Detection—0
Logarithmic Lenses: Exploring Log RGB Data for Image Classification—0
Logic-Driven Context Extension and Data Augmentation for Logical Reasoning of Text—0
Logic Guided Genetic Algorithms—0
Logit As Auxiliary Weak-supervision for More Reliable and Accurate Prediction—0
Longitudinal detection of new MS lesions using Deep Learning—0
Long-Tailed Backdoor Attack Using Dynamic Data Augmentation Operations—0
Long-Tailed Continual Learning For Visual Food Recognition—0
Long-tailed Food Classification—0
Long-tailed Recognition by Learning from Latent Categories—0
Long-Term Modeling of Financial Machine Learning for Active Portfolio Management—0
Long Term Object Detection and Tracking in Collaborative Learning Environments—0
Long-time predictive modeling of nonlinear dynamical systems using neural networks—0
Lorentz Local Canonicalization: How to Make Any Network Lorentz-Equivariant—0
LOST: A Mental Health Dataset of Low Self-esteem in Reddit Posts—0
Lost in Translation: Modern Neural Networks Still Struggle With Small Realistic Image Transformations—0
Lost in Transmission: On the Impact of Networking Corruptions on Video Machine Learning Models—0
Low-Complexity Acoustic Scene Classification Using Data Augmentation and Lightweight ResNet—0
Low-complexity deep learning frameworks for acoustic scene classification—0
Low-Complexity Own Voice Reconstruction for Hearables with an In-Ear Microphone—0
Low-cost and high-performance data augmentation for deep-learning-based skin lesion classification—0
Low-data? No problem: low-resource, language-agnostic conversational text-to-speech via F0-conditioned data augmentation—0
Low-rank representation of head impact kinematics: A data-driven emulator—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 2×96d) (Faster AA)Percentage error2—Unverified
5Shake-Shake (26 2×112d) (Faster AA)Percentage error2—Unverified
#ModelMetricClaimedVerifiedStatus
1DiffAugClassification Accuracy92.7—Unverified
2PaCMAPClassification Accuracy85.3—Unverified
3hNNEClassification Accuracy77.4—Unverified
4TopoAEClassification Accuracy74.6—Unverified