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 1701–1750 of 8378 papers

TitleStatusHype
Leveraging Diffusion Models for Synthetic Data Augmentation in Protein Subcellular Localization Classification—0
Supervised Contrastive Learning for Ordinal Engagement Measurement—0
Towards Pretraining Robust ASR Foundation Model with Acoustic-Aware Data Augmentation—0
Detection of Suicidal Risk on Social Media: A Hybrid Model—0
Lorentz Local Canonicalization: How to Make Any Network Lorentz-Equivariant—0
Enhancing Contrastive Learning-based Electrocardiogram Pretrained Model with Patient Memory QueueCode0
ControlTac: Force- and Position-Controlled Tactile Data Augmentation with a Single Reference Image—0
Revolutionizing Wildfire Detection with Convolutional Neural Networks: A VGG16 Model Approach—0
Improving Heart Rejection Detection in XPCI Images Using Synthetic Data Augmentation—0
A Joint Learning Framework with Feature Reconstruction and Prediction for Incomplete Satellite Image Time Series in Agricultural Semantic SegmentationCode0
Learn Beneficial Noise as Graph Augmentation—0
Building a Functional Machine Translation Corpus for Kpelle—0
Beyond Domain Randomization: Event-Inspired Perception for Visually Robust Adversarial Imitation from VideosCode0
The Quest for Efficient Reasoning: A Data-Centric Benchmark to CoT Distillation—0
What Do You Need for Diverse Trajectory Stitching in Diffusion Planning?—0
Audio-to-Audio Emotion Conversion With Pitch And Duration Style Transfer—0
Large language model as user daily behavior data generator: balancing population diversity and individual personality—0
Supervised Graph Contrastive Learning for Gene Regulatory Network—0
Swin Transformer for Robust CGI Images Detection: Intra- and Inter-Dataset Analysis across Multiple Color Spaces—0
Maximum Total Correlation Reinforcement LearningCode0
Does Synthetic Data Help Named Entity Recognition for Low-Resource Languages?—0
Efficient Prototype Consistency Learning in Medical Image Segmentation via Joint Uncertainty and Data Augmentation—0
Aug2Search: Enhancing Facebook Marketplace Search with LLM-Generated Synthetic Data Augmentation—0
Geometrically Regularized Transfer Learning with On-Manifold and Off-Manifold Perturbation—0
GAMA: Geometry-Aware Manifold Alignment via Structured Adversarial Perturbations for Robust Domain Adaptation—0
Human-centered Interactive Learning via MLLMs for Text-to-Image Person Re-identification—0
Data Augmentation and Resolution Enhancement using GANs and Diffusion Models for Tree Segmentation—0
15,500 Seconds: Lean UAV Classification Leveraging PEFT and Pre-Trained NetworksCode0
Mixing times of data-augmentation Gibbs samplers for high-dimensional probit regressionCode0
SSPS: Self-Supervised Positive Sampling for Robust Self-Supervised Speaker Verification—0
Truth or Twist? Optimal Model Selection for Reliable Label Flipping Evaluation in LLM-based Counterfactuals—0
Challenges and Limitations in the Synthetic Generation of mHealth Sensor Data—0
GSDFuse: Capturing Cognitive Inconsistencies from Multi-Dimensional Weak Signals in Social Media SteganalysisCode0
Data-Efficient Hate Speech Detection via Cross-Lingual Nearest Neighbor Retrieval with Limited Labeled Data—0
PEER pressure: Model-to-Model Regularization for Single Source Domain Generalization—0
SMOTExT: SMOTE meets Large Language ModelsCode0
On the Mechanisms of Adversarial Data Augmentation for Robust and Adaptive Transfer Learning—0
Safety Alignment Can Be Not Superficial With Explicit Safety Signals—0
Anti-Inpainting: A Proactive Defense against Malicious Diffusion-based Inpainters under Unknown Conditions—0
Segmentation of temporomandibular joint structures on mri images using neural networks for diagnosis of pathologies—0
An approach based on class activation maps for investigating the effects of data augmentation on neural networks for image classification—0
Informed Mixing -- Improving Open Set Recognition via Attribution-based Augmentation—0
AutoMathKG: The automated mathematical knowledge graph based on LLM and vector database—0
Attention-Enhanced U-Net for Accurate Segmentation of COVID-19 Infected Lung Regions in CT Scans—0
Joint Embedding vs Reconstruction: Provable Benefits of Latent Space Prediction for Self Supervised Learning—0
SGD-Mix: Enhancing Domain-Specific Image Classification with Label-Preserving Data Augmentation—0
Facial Recognition Leveraging Generative Adversarial Networks—0
Relation-Aware Graph Foundation Model—0
Towards Cultural Bridge by Bahnaric-Vietnamese Translation Using Transfer Learning of Sequence-To-Sequence Pre-training Language Model—0
PhiNet v2: A Mask-Free Brain-Inspired Vision Foundation Model from VideoCode0
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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