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 2451–2500 of 8378 papers

TitleStatusHype
Revisiting Logistic-softmax Likelihood in Bayesian Meta-Learning for Few-Shot ClassificationCode1
BanglaNLP at BLP-2023 Task 1: Benchmarking different Transformer Models for Violence Inciting Text Detection in Bengali—0
Gaussian processes based data augmentation and expected signature for time series classification—0
Will the Prince Get True Love's Kiss? On the Model Sensitivity to Gender Perturbation over Fairytale Texts—0
Contextual Data Augmentation for Task-Oriented Dialog Systems—0
Towards the Imagenets of ML4EDA—0
Data Augmentation for Time-Series Classification: An Extensive Empirical Study and Comprehensive Survey—0
AdaptSSR: Pre-training User Model with Augmentation-Adaptive Self-Supervised RankingCode0
Enhancing ML model accuracy for Digital VLSI circuits using diffusion models: A study on synthetic data generation—0
AugUndo: Scaling Up Augmentations for Monocular Depth Completion and EstimationCode0
SGA: A Graph Augmentation Method for Signed Graph Neural Networks—0
Class-Specific Data Augmentation: Bridging the Imbalance in Multiclass Breast Cancer Classification—0
Image Augmentation with Controlled Diffusion for Weakly-Supervised Semantic Segmentation—0
Generalizing Few-Shot Named Entity Recognizers to Unseen Domains with Type-Related FeaturesCode0
A study of the impact of generative AI-based data augmentation on software metadata classification—0
Assessing and Enhancing the Robustness of Large Language Models with Task Structure Variations for Logical ReasoningCode1
Equirectangular image construction method for standard CNNs for Semantic Segmentation—0
CLExtract: Recovering Highly Corrupted DVB/GSE Satellite Stream with Contrastive Learning—0
Enhancing Text-based Knowledge Graph Completion with Zero-Shot Large Language Models: A Focus on Semantic EnhancementCode1
CleftGAN: Adapting A Style-Based Generative Adversarial Network To Create Images Depicting Cleft Lip DeformityCode0
SEE-OoD: Supervised Exploration For Enhanced Out-of-Distribution Detection—0
Revisiting Data Augmentation for Rotational Invariance in Convolutional Neural Networks—0
DualAug: Exploiting Additional Heavy Augmentation with OOD Data RejectionCode0
Line Detection and Segmentation of Annual Crops Using Hybrid MethodCode0
Does Synthetic Data Make Large Language Models More Efficient?—0
ADASR: An Adversarial Auto-Augmentation Framework for Hyperspectral and Multispectral Data FusionCode1
Revisiting Plasticity in Visual Reinforcement Learning: Data, Modules and Training StagesCode1
Diagnosing Bipolar Disorder from 3-D Structural Magnetic Resonance Images Using a Hybrid GAN-CNN Method—0
Self-supervised Representation Learning From Random Data ProjectorsCode1
DrugCLIP: Contrastive Protein-Molecule Representation Learning for Virtual ScreeningCode1
What Makes for Robust Multi-Modal Models in the Face of Missing Modalities?—0
No Pitch Left Behind: Addressing Gender Unbalance in Automatic Speech Recognition through Pitch Manipulation—0
Revisit Input Perturbation Problems for LLMs: A Unified Robustness Evaluation Framework for Noisy Slot Filling TaskCode1
Domain Generalization by Rejecting Extreme AugmentationsCode0
Tertiary Lymphoid Structures Generation through Graph-based Diffusion—0
Empirical Evaluation of the Segment Anything Model (SAM) for Brain Tumor Segmentation—0
Augmenting Vision-Based Human Pose Estimation with Rotation Matrix—0
UAVs and Neural Networks for search and rescue missions—0
MuggleMath: Assessing the Impact of Query and Response Augmentation on Math ReasoningCode2
Resolving the Imbalance Issue in Hierarchical Disciplinary Topic Inference via LLM-based Data Augmentation—0
Cross-Task Data Augmentation by Pseudo-label Generation for Region Based Coronary Artery Instance Segmentation—0
MinPrompt: Graph-based Minimal Prompt Data Augmentation for Few-shot Question AnsweringCode1
Cross-head mutual Mean-Teaching for semi-supervised medical image segmentationCode1
IPMix: Label-Preserving Data Augmentation Method for Training Robust ClassifiersCode1
Offline Imitation Learning with Variational Counterfactual ReasoningCode0
Parameterizing Context: Unleashing the Power of Parameter-Efficient Fine-Tuning and In-Context Tuning for Continual Table Semantic Parsing—0
Whole Slide Multiple Instance Learning for Predicting Axillary Lymph Node MetastasisCode0
Accelerated Neural Network Training with Rooted Logistic Objectives—0
Latent Filling: Latent Space Data Augmentation for Zero-shot Speech Synthesis—0
Untargeted White-box Adversarial Attack with Heuristic Defence Methods in Real-time Deep Learning based Network Intrusion Detection System—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