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:

( Image credit: Albumentations )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 5901–5950 of 8378 papers

TitleStatusHype
Whisper Finetuning on Nepali Language—0
Whisper Turns Stronger: Augmenting Wav2Vec 2.0 for Superior ASR in Low-Resource Languages—0
White-box Testing of NLP models with Mask Neuron Coverage—0
White Light Specular Reflection Data Augmentation for Deep Learning Polyp Detection—0
Who is we? Disambiguating the referents of first person plural pronouns in parliamentary debates—0
Who Is Your Right Mixup Partner in Positive and Unlabeled Learning—0
Whole-Slide Mitosis Detection in H&E Breast Histology Using PHH3 as a Reference to Train Distilled Stain-Invariant Convolutional Networks—0
Why does music source separation benefit from cacophony?—0
Why Pre-trained Models Fail: Feature Entanglement in Multi-modal Depression Detection—0
WideResNet with Joint Representation Learning and Data Augmentation for Cover Song Identification—0
Will the Prince Get True Love's Kiss? On the Model Sensitivity to Gender Perturbation over Fairytale Texts—0
Winning Amazon KDD Cup'24—0
Wireless Channel Aware Data Augmentation Methods for Deep Learning-Based Indoor Localization—0
Without Further Ado: Direct and Simultaneous Speech Translation by AppTek in 2021—0
WMD at SemEval-2020 Tasks 7 and 11: Assessing Humor and Propaganda Using Unsupervised Data Augmentation—0
Workflow Augmentation of Video Data for Event Recognition with Time-Sensitive Neural Networks—0
World-Consistent Data Generation for Vision-and-Language Navigation—0
WoundAmbit: Bridging State-of-the-Art Semantic Segmentation and Real-World Wound Care—0
WQT and DG-YOLO: towards domain generalization in underwater object detection—0
WSSS4LUAD: Grand Challenge on Weakly-supervised Tissue Semantic Segmentation for Lung Adenocarcinoma—0
WUY at SemEval-2020 Task 7: Combining BERT and Naive Bayes-SVM for Humor Assessment in Edited News Headlines—0
XAIQA: Explainer-Based Data Augmentation for Extractive Question Answering—0
XCAT -- Lightweight Quantized Single Image Super-Resolution using Heterogeneous Group Convolutions and Cross Concatenation—0
X-CNN: Cross-modal Convolutional Neural Networks for Sparse Datasets—0
Xeno-learning: knowledge transfer across species in deep learning-based spectral image analysis—0
XGPT: Cross-modal Generative Pre-Training for Image Captioning—0
XLA: A Robust Unsupervised Data Augmentation Framework for Cross-Lingual NLP—0
XLDA: Cross-Lingual Data Augmentation for Natural Language Inference and Question Answering—0
XLST: Cross-lingual Self-training to Learn Multilingual Representation for Low Resource Speech Recognition—0
XOR Mixup: Privacy-Preserving Data Augmentation for One-Shot Federated Learning—0
XPose: eXplainable Human Pose Estimation—0
XSYSIGMA at SemEval-2020 Task 7: Method for Predicting Headlines' Humor Based on Auxiliary Sentences with EI-BERT—0
Yes, we GAN: Applying Adversarial Techniques for Autonomous Driving—0
YOLO-ELA: Efficient Local Attention Modeling for High-Performance Real-Time Insulator Defect Detection—0
YOLOv4: A Breakthrough in Real-Time Object Detection—0
YOLOv5s-GTB: light-weighted and improved YOLOv5s for bridge crack detection—0
You Do Not Need More Data: Improving End-To-End Speech Recognition by Text-To-Speech Data Augmentation—0
You Don't Have to Be Perfect to Be Amazing: Unveil the Utility of Synthetic Images—0
You Don't Need Domain-Specific Data Augmentations When Scaling Self-Supervised Learning—0
Your Image is My Video: Reshaping the Receptive Field via Image-To-Video Differentiable AutoAugmentation and Fusion—0
Your Image is Secretly the Last Frame of a Pseudo Video—0
Your Language Model May Think Too Rigidly: Achieving Reasoning Consistency with Symmetry-Enhanced Training—0
YuruGAN: Yuru-Chara Mascot Generator Using Generative Adversarial Networks With Clustering Small Dataset—0
Zero-pronoun Data Augmentation for Japanese-to-English Translation—0
ZeroShotDataAug: Generating and Augmenting Training Data with ChatGPT—0
Zero-shot domain adaptation based on dual-level mix and contrast—0
Zero-Shot Generalization of Vision-Based RL Without Data Augmentation—0
Integrating the Data Augmentation Scheme with Various Classifiers for Acoustic Scene Modeling—0
Intelligent Crack Detection and Quantification in the Concrete Bridge: A Deep Learning-Assisted Image Processing Approach—0
Intent Detection in the Age of LLMs—0
Show:102550
← PrevPage 119 of 168Next →
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