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 4801–4850 of 8378 papers

TitleStatusHype
Rock Classification Based on Residual Networks—0
RocketQA: An Optimized Training Approach to Dense Passage Retrieval for Open-Domain Question Answering—0
RocketQAv2: A Joint Training Method for Dense Passage Retrieval and Passage Re-ranking—0
RoCNet: 3D Robust Registration of Point-Clouds using Deep Learning—0
RoCoDA: Counterfactual Data Augmentation for Data-Efficient Robot Learning from Demonstrations—0
RoFIR: Robust Fisheye Image Rectification Framework Impervious to Optical Center Deviation—0
ROI-10D: Monocular Lifting of 2D Detection to 6D Pose and Metric Shape—0
Role of Mixup in Topological Persistence Based Knowledge Distillation for Wearable Sensor Data—0
Rollable Latent Space for Azimuth Invariant SAR Target Recognition—0
Rolling the dice for better deep learning performance: A study of randomness techniques in deep neural networks—0
ROMUL: Scale Adaptative Population Based Training—0
RoPDA: Robust Prompt-based Data Augmentation for Low-Resource Named Entity Recognition—0
Rotating spiders and reflecting dogs: a class conditional approach to learning data augmentation distributions—0
Rotational augmentation techniques: a new perspective on ensemble learning for image classification—0
Rotation Invariance and Extensive Data Augmentation: a strategy for the Mitosis Domain Generalization (MIDOG) Challenge—0
Rotation-Invariant Local-to-Global Representation Learning for 3D Point Cloud—0
RotEqNet: Rotation-Equivariant Network for Fluid Systems with Symmetric High-Order Tensors—0
RRM: Robust Reward Model Training Mitigates Reward Hacking—0
Rubric-Specific Approach to Automated Essay Scoring with Augmentation Training—0
Rumour detection using graph neural network and oversampling in benchmark Twitter dataset—0
RV-Syn: Rational and Verifiable Mathematical Reasoning Data Synthesis based on Structured Function Library—0
S2M: Converting Single-Turn to Multi-Turn Datasets for Conversational Question Answering—0
S3Aug: Segmentation, Sampling, and Shift for Action Recognition—0
S^4M: Boosting Semi-Supervised Instance Segmentation with SAM—0
S4RL: Surprisingly Simple Self-Supervision for Offline Reinforcement Learning—0
SA-Attack: Improving Adversarial Transferability of Vision-Language Pre-training Models via Self-Augmentation—0
SafeAug: Safety-Critical Driving Data Augmentation from Naturalistic Datasets—0
SAFE setup for generative molecular design—0
Safety Alignment Can Be Not Superficial With Explicit Safety Signals—0
Safety Enhancement for Deep Reinforcement Learning in Autonomous Separation Assurance—0
SAFLEX: Self-Adaptive Augmentation via Feature Label Extrapolation—0
SAGE-HB: Swift Adaptation and Generalization in Massive MIMO Hybrid Beamforming—0
SAGE: Saliency-Guided Mixup with Optimal Rearrangements—0
SAG-GAN: Semi-Supervised Attention-Guided GANs for Data Augmentation on Medical Images—0
SAICL: Student Modelling with Interaction-level Auxiliary Contrastive Tasks for Knowledge Tracing and Dropout Prediction—0
Saliency-Based diversity and fairness Metric and FaceKeepOriginalAugment: A Novel Approach for Enhancing Fairness and Diversity—0
Saliency-based Multiple Region of Interest Detection from a Single 360° image—0
SaliencyCut: Augmenting Plausible Anomalies for Anomaly Detection—0
Saliency Map Based Data Augmentation—0
Salient Slices: Improved Neural Network Training and Performance with Image Entropy—0
Sample Dropout for Audio Scene Classification Using Multi-Scale Dense Connected Convolutional Neural Network—0
Sample Efficiency of Data Augmentation Consistency Regularization—0
Sample-Efficient Reinforcement Learning via Counterfactual-Based Data Augmentation—0
Sample-specific and Context-aware Augmentation for Long Tail Image Classification—0
Sample, Translate, Recombine: Leveraging Audio Alignments for Data Augmentation in End-to-end Speech Translation—0
SanitAIs: Unsupervised Data Augmentation to Sanitize Trojaned Neural Networks—0
SANN-PSZ: Spatially Adaptive Neural Network for Head-Tracked Personal Sound Zones—0
SapAugment: Learning A Sample Adaptive Policy for Data Augmentation—0
Sarcasm and Sentiment Detection In Arabic Tweets Using BERT-based Models and Data Augmentation—0
SASMU: boost the performance of generalized recognition model using synthetic face dataset—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