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 2501–2550 of 8378 papers

TitleStatusHype
Empirical Evaluation of the Segment Anything Model (SAM) for Brain Tumor Segmentation—0
Challenges and Opportunities for Machine Learning Classification of Behavior and Mental State from Images—0
Challenges and Limitations in the Synthetic Generation of mHealth Sensor Data—0
Chain-of-Thought Prompt Distillation for Multimodal Named Entity Recognition and Multimodal Relation Extraction—0
Chain of History: Learning and Forecasting with LLMs for Temporal Knowledge Graph Completion—0
EMIXER: End-to-end Multimodal X-ray Generation via Self-supervision—0
Read and Think: An Efficient Step-wise Multimodal Language Model for Document Understanding and Reasoning—0
Certifying Adapters: Enabling and Enhancing the Certification of Classifier Adversarial Robustness—0
Are Deep Learning Models Robust to Partial Object Occlusion in Visual Recognition Tasks?—0
Certified Adversarial Defenses Meet Out-of-Distribution Corruptions: Benchmarking Robustness and Simple Baselines—0
Adversarial VQA: A New Benchmark for Evaluating the Robustness of VQA Models—0
EmoDiarize: Speaker Diarization and Emotion Identification from Speech Signals using Convolutional Neural Networks—0
End-to-end Global to Local CNN Learning for Hand Pose Recovery in Depth Data—0
Enhanced Image Classification With Data Augmentation Using Position Coordinates—0
Center-wise Local Image Mixture For Contrastive Representation Learning—0
Center-aware Adversarial Augmentation for Single Domain Generalization—0
A Recurrent YOLOv8-based framework for Event-Based Object Detection—0
Are Current Task-oriented Dialogue Systems Able to Satisfy Impolite Users?—0
Adversarial Unlearning: Reducing Confidence Along Adversarial Directions—0
Embedding Space Augmentation for Weakly Supervised Learning in Whole-Slide Images—0
CEKD:Cross Ensemble Knowledge Distillation for Augmented Fine-grained Data—0
Are conditional GANs explicitly conditional?—0
Adversarial Training for Patient-Independent Feature Learning with IVOCT Data for Plaque Classification—0
CDUPatch: Color-Driven Universal Adversarial Patch Attack for Dual-Modal Visible-Infrared Detectors—0
CDS: Data Synthesis Method Guided by Cognitive Diagnosis Theory—0
NeuralSurv: Deep Survival Analysis with Bayesian Uncertainty Quantification—0
Embedding Space Interpolation Beyond Mini-Batch, Beyond Pairs and Beyond Examples—0
CCFace: Classification Consistency for Low-Resolution Face Recognition—0
A Recipe for Efficient SBIR Models: Combining Relative Triplet Loss with Batch Normalization and Knowledge Distillation—0
Adversarial synthesis based data-augmentation for code-switched spoken language identification—0
CAUSE: Counterfactual Assessment of User Satisfaction Estimation in Task-Oriented Dialogue Systems—0
Mixture Data for Training Cannot Ensure Out-of-distribution Generalization—0
Data Augmentations for Improved (Large) Language Model Generalization—0
Causal Reasoning: Charting a Revolutionary Course for Next-Generation AI-Native Wireless Networks—0
Emad at SemEval-2019 Task 6: Offensive Language Identification using Traditional Machine Learning and Deep Learning approaches—0
Embarrassingly Simple MixUp for Time-series—0
Causal Prompting: Debiasing Large Language Model Prompting based on Front-Door Adjustment—0
Adversarial Style Transfer for Robust Policy Optimization in Deep Reinforcement Learning—0
Elastic deformation of optical coherence tomography images of diabetic macular edema for deep-learning models training: how far to go?—0
Interpreting the Robustness of Neural NLP Models to Textual Perturbations—0
ArCL: Enhancing Contrastive Learning with Augmentation-Robust Representations—0
Leveraging Contextual Data Augmentation for Generalizable Melanoma Detection—0
Causal Information Prioritization for Efficient Reinforcement Learning—0
CausalDyna: Improving Generalization of Dyna-style Reinforcement Learning via Counterfactual-Based Data Augmentation—0
Adversarial Semantic Augmentation for Training Generative Adversarial Networks under Limited Data—0
EG-ConMix: An Intrusion Detection Method based on Graph Contrastive Learning—0
Egocentric Gesture Recognition for Head-Mounted AR devices—0
ELMO: Enhanced Real-time LiDAR Motion Capture through Upsampling—0
Category-Level 6D Object Pose Estimation with Flexible Vector-Based Rotation Representation—0
Category-Learning with Context-Augmented Autoencoder—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