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.

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 2476–2500 of 8378 papers

Title	Date	Tasks	Status	Hype
ADASR: An Adversarial Auto-Augmentation Framework for Hyperspectral and Multispectral Data Fusion	Oct 11, 2023	Data AugmentationDiversity	CodeCode Available	1
Revisiting Plasticity in Visual Reinforcement Learning: Data, Modules and Training Stages	Oct 11, 2023	Data Augmentationreinforcement-learning	CodeCode Available	1
Diagnosing Bipolar Disorder from 3-D Structural Magnetic Resonance Images Using a Hybrid GAN-CNN Method	Oct 11, 2023	Data AugmentationEEG	—Unverified	0
Self-supervised Representation Learning From Random Data Projectors	Oct 11, 2023	Data AugmentationRepresentation Learning	CodeCode Available	1
DrugCLIP: Contrastive Protein-Molecule Representation Learning for Virtual Screening	Oct 10, 2023	Contrastive LearningData Augmentation	CodeCode Available	1
What Makes for Robust Multi-Modal Models in the Face of Missing Modalities?	Oct 10, 2023	Data Augmentation	—Unverified	0
No Pitch Left Behind: Addressing Gender Unbalance in Automatic Speech Recognition through Pitch Manipulation	Oct 10, 2023	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified	0
Revisit Input Perturbation Problems for LLMs: A Unified Robustness Evaluation Framework for Noisy Slot Filling Task	Oct 10, 2023	Data AugmentationDialogue Understanding	CodeCode Available	1
Domain Generalization by Rejecting Extreme Augmentations	Oct 10, 2023	Data AugmentationDomain Generalization	CodeCode Available	0
Tertiary Lymphoid Structures Generation through Graph-based Diffusion	Oct 10, 2023	Data Augmentation	—Unverified	0
Empirical Evaluation of the Segment Anything Model (SAM) for Brain Tumor Segmentation	Oct 9, 2023	Brain Tumor SegmentationData Augmentation	—Unverified	0
Augmenting Vision-Based Human Pose Estimation with Rotation Matrix	Oct 9, 2023	Activity RecognitionData Augmentation	—Unverified	0
UAVs and Neural Networks for search and rescue missions	Oct 9, 2023	Data Augmentationobject-detection	—Unverified	0
MuggleMath: Assessing the Impact of Query and Response Augmentation on Math Reasoning	Oct 9, 2023	Arithmetic ReasoningData Augmentation	CodeCode Available	2
Resolving the Imbalance Issue in Hierarchical Disciplinary Topic Inference via LLM-based Data Augmentation	Oct 9, 2023	Data AugmentationFairness	—Unverified	0
Cross-Task Data Augmentation by Pseudo-label Generation for Region Based Coronary Artery Instance Segmentation	Oct 8, 2023	Coronary Artery SegmentationData Augmentation	—Unverified	0
MinPrompt: Graph-based Minimal Prompt Data Augmentation for Few-shot Question Answering	Oct 8, 2023	Data AugmentationQuestion Answering	CodeCode Available	1
Cross-head mutual Mean-Teaching for semi-supervised medical image segmentation	Oct 8, 2023	Data AugmentationImage Segmentation	CodeCode Available	1
IPMix: Label-Preserving Data Augmentation Method for Training Robust Classifiers	Oct 7, 2023	Anomaly DetectionData Augmentation	CodeCode Available	1
Offline Imitation Learning with Variational Counterfactual Reasoning	Oct 7, 2023	counterfactualCounterfactual Inference	CodeCode Available	0
Parameterizing Context: Unleashing the Power of Parameter-Efficient Fine-Tuning and In-Context Tuning for Continual Table Semantic Parsing	Oct 7, 2023	Continual LearningData Augmentation	—Unverified	0
Whole Slide Multiple Instance Learning for Predicting Axillary Lymph Node Metastasis	Oct 6, 2023	Data AugmentationMultiple Instance Learning	CodeCode Available	0
Accelerated Neural Network Training with Rooted Logistic Objectives	Oct 5, 2023	Binary ClassificationData Augmentation	—Unverified	0
Latent Filling: Latent Space Data Augmentation for Zero-shot Speech Synthesis	Oct 5, 2023	Data AugmentationSpeech Synthesis	—Unverified	0
Untargeted White-box Adversarial Attack with Heuristic Defence Methods in Real-time Deep Learning based Network Intrusion Detection System	Oct 5, 2023	Adversarial AttackData Augmentation	—Unverified	0

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All datasets ImageNet CIFAR-10 GA1457

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	DeiT-B (+MixPro)	Accuracy (%)	82.9	—	Unverified
2	ResNet-200 (DeepAA)	Accuracy (%)	81.32	—	Unverified
3	DeiT-S (+MixPro)	Accuracy (%)	81.3	—	Unverified
4	ResNet-200 (Fast AA)	Accuracy (%)	80.6	—	Unverified
5	ResNet-200 (UA)	Accuracy (%)	80.4	—	Unverified
6	ResNet-200 (AA)	Accuracy (%)	80	—	Unverified
7	ResNet-50 (DeepAA)	Accuracy (%)	78.3	—	Unverified
8	ResNet-50 (TA wide)	Accuracy (%)	78.07	—	Unverified
9	ResNet-50 (LoRot-E)	Accuracy (%)	77.72	—	Unverified
10	ResNet-50 (LoRot-I)	Accuracy (%)	77.71	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	WideResNet-40-2 (Faster AA)	Percentage error	3.7	—	Unverified
2	Shake-Shake (26 2×32d) (Faster AA)	Percentage error	2.7	—	Unverified
3	WideResNet-28-10 (Faster AA)	Percentage error	2.6	—	Unverified
4	Shake-Shake (26 2×112d) (Faster AA)	Percentage error	2	—	Unverified
5	Shake-Shake (26 2×96d) (Faster AA)	Percentage error	2	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	DiffAug	Classification Accuracy	92.7	—	Unverified
2	PaCMAP	Classification Accuracy	85.3	—	Unverified
3	hNNE	Classification Accuracy	77.4	—	Unverified
4	TopoAE	Classification Accuracy	74.6	—	Unverified