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 4351–4400 of 8378 papers

TitleStatusHype
Optimizing Sentence Embedding with Pseudo-Labeling and Model Ensembles: A Hierarchical Framework for Enhanced NLP Tasks—0
Optimizing the AI Development Process by Providing the Best Support Environment—0
Order Doesn't Matter, But Reasoning Does: Training LLMs with Order-Centric Augmentation—0
Order-sensitive Shapley Values for Evaluating Conceptual Soundness of NLP Models—0
orGAN: A Synthetic Data Augmentation Pipeline for Simultaneous Generation of Surgical Images and Ground Truth Labels—0
Original or Translated? On the Use of Parallel Data for Translation Quality Estimation—0
Ortho-Shot: Low Displacement Rank Regularization with Data Augmentation for Few-Shot Learning—0
OR-UNet: an Optimized Robust Residual U-Net for Instrument Segmentation in Endoscopic Images—0
OT-Attack: Enhancing Adversarial Transferability of Vision-Language Models via Optimal Transport Optimization—0
Outlier-aware Tensor Robust Principal Component Analysis with Self-guided Data Augmentation—0
Test-Time Fairness and Robustness in Large Language Models—0
Output Feedback Tube MPC-Guided Data Augmentation for Robust, Efficient Sensorimotor Policy Learning—0
Overcoming Data Scarcity in Generative Language Modelling for Low-Resource Languages: A Systematic Review—0
Overcoming limited battery data challenges: A coupled neural network approach—0
Overlapping Word Removal is All You Need: Revisiting Data Imbalance in Hope Speech Detection—0
Student Specialization in Deep ReLU Networks With Finite Width and Input Dimension—0
Overview of the TalentCLEF 2025: Skill and Job Title Intelligence for Human Capital Management—0
P^2 Net: Augmented Parallel-Pyramid Net for Attention Guided Pose Estimation—0
PAC Learnability under Explanation-Preserving Graph Perturbations—0
PAC-tuning:Fine-tuning Pretrained Language Models with PAC-driven Perturbed Gradient Descent—0
Paired Cross-Modal Data Augmentation for Fine-Grained Image-to-Text Retrieval—0
PALI at SemEval-2021 Task 2: Fine-Tune XLM-RoBERTa for Word in Context Disambiguation—0
PaMMA-Net: Plasmas magnetic measurement evolution based on data-driven incremental accumulative prediction—0
MAC: A unified framework boosting low resource automatic speech recognition—0
PANDA: AdaPtive Noisy Data Augmentation for Regularization of Undirected Graphical Models—0
PanDA: Panoptic Data Augmentation—0
PanoMixSwap Panorama Mixing via Structural Swapping for Indoor Scene Understanding—0
Panoptic Out-of-Distribution Segmentation—0
Parallel Recurrent Data Augmentation for GAN training with Limited and Diverse Data—0
Parallel resources for Tunisian Arabic Dialect Translation—0
Parameter Efficient Audio Captioning With Faithful Guidance Using Audio-text Shared Latent Representation—0
Parameterizing Context: Unleashing the Power of Parameter-Efficient Fine-Tuning and In-Context Tuning for Continual Table Semantic Parsing—0
Parametric Implicit Face Representation for Audio-Driven Facial Reenactment—0
Parametric Variational Linear Units (PVLUs) in Deep Convolutional Networks—0
Paraphrasing via Ranking Many Candidates—0
ParaZh-22M: A Large-Scale Chinese Parabank via Machine Translation—0
Partial differential equation regularization for supervised machine learning—0
Partial Face Detection in the Mobile Domain—0
Partially fake it till you make it: mixing real and fake thermal images for improved object detection—0
ParticleAugment: Sampling-Based Data Augmentation—0
Parting with Illusions about Deep Active Learning—0
Partitioning Image Representation in Contrastive Learning—0
PartMix: Regularization Strategy to Learn Part Discovery for Visible-Infrared Person Re-identification—0
Part Segmentation for Highly Accurate Deformable Tracking in Occlusions via Fully Convolutional Neural Networks—0
PASS3D: Precise and Accelerated Semantic Segmentation for 3D Point Cloud—0
PASTS: Progress-Aware Spatio-Temporal Transformer Speaker For Vision-and-Language Navigation—0
Patch-aware Batch Normalization for Improving Cross-domain Robustness—0
PatchMix Augmentation to Identify Causal Features in Few-shot Learning—0
Patch Reordering: a Novel Way to Achieve Rotation and Translation Invariance in Convolutional Neural Networks—0
Patch Stitching Data Augmentation for Cancer Classification in Pathology Images—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