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 2601–2650 of 8378 papers

TitleStatusHype
Enhancing Nighttime Vehicle Detection with Day-to-Night Style Transfer and Labeling-Free Augmentation—0
Enhancing Task-Specific Distillation in Small Data Regimes through Language Generation—0
eRock at Qur’an QA 2022: Contemporary Deep Neural Networks for Qur’an based Reading Comprehension Question Answers—0
Application of Transfer Learning and Ensemble Learning in Image-level Classification for Breast Histopathology—0
Can Self Supervision Rejuvenate Similarity-Based Link Prediction?—0
Acoustic and Textual Data Augmentation for Improved ASR of Code-Switching Speech—0
Can segmentation models be trained with fully synthetically generated data?—0
Can Question Generation Debias Question Answering Models? A Case Study on Question–Context Lexical Overlap—0
Application of multilayer perceptron with data augmentation in nuclear physics—0
Enhancing Generalization in Medical Visual Question Answering Tasks via Gradient-Guided Model Perturbation—0
Application of Mix-Up Method in Document Classification Task Using BERT—0
Can physical information aid the generalization ability of Neural Networks for hydraulic modeling?—0
Enhancing Few-Shot Learning with Integrated Data and GAN Model Approaches—0
Can Open-source LLMs Enhance Data Synthesis for Toxic Detection?: An Experimental Study—0
Application of Deep Learning Methods to SNOMED CT Encoding of Clinical Texts: From Data Collection to Extreme Multi-Label Text-Based Classification—0
Adversarially Optimized Mixup for Robust Classification—0
Enhancing Few-shot NER with Prompt Ordering based Data Augmentation—0
Enhancing Glass Defect Detection with Diffusion Models: Addressing Imbalanced Datasets in Manufacturing Quality Control—0
CAN-LOC: Spoofing Detection and Physical Intrusion Localization on an In-Vehicle CAN Bus Based on Deep Features of Voltage Signals—0
Application of Deep Learning in Neuroradiology: Automated Detection of Basal Ganglia Hemorrhage using 2D-Convolutional Neural Networks—0
Adversarial Learning for Neural PDE Solvers with Sparse Data—0
Can Language Models Take A Hint? Prompting for Controllable Contextualized Commonsense Inference—0
Can Language Models Take A Hint? Prompting for Controllable Contextualized Commonsense Inference—0
Learning Test-time Augmentation for Content-based Image Retrieval—0
Can I Be of Further Assistance? Using Unstructured Knowledge Access to Improve Task-oriented Conversational Modeling—0
APOLLO: A Simple Approach for Adaptive Pretraining of Language Models for Logical Reasoning—0
Enhancing Eye Disease Diagnosis with Deep Learning and Synthetic Data Augmentation—0
Can Deep Learning Trigger Alerts from Mobile-Captured Images?—0
ACoRN: Noise-Robust Abstractive Compression in Retrieval-Augmented Language Models—0
Enhancing Facial Data Diversity with Style-based Face Aging—0
Enhancing Fetal Plane Classification Accuracy with Data Augmentation Using Diffusion Models—0
Enhancing Graph Contrastive Learning with Node Similarity—0
Cancer image classification based on DenseNet model—0
Can a Transformer Pass the Wug Test? Tuning Copying Bias in Neural Morphological Inflection Models—0
A Point-Neighborhood Learning Framework for Nasal Endoscope Image Segmentation—0
Camouflaged Chinese Spam Content Detection with Semi-supervised Generative Active Learning—0
A2Log: Attentive Augmented Log Anomaly Detection—0
Reconstructing Syllable Sequences in Abugida Scripts with Incomplete Inputs—0
Enhancing DR Classification with Swin Transformer and Shifted Window Attention—0
CameraPose: Weakly-Supervised Monocular 3D Human Pose Estimation by Leveraging In-the-wild 2D Annotations—0
Cambridge at SemEval-2021 Task 2: Neural WiC-Model with Data Augmentation and Exploration of Representation—0
Call-sign recognition and understanding for noisy air-traffic transcripts using surveillance information—0
A Picture May Be Worth a Hundred Words for Visual Question Answering—0
Enhancing EEG Signal Generation through a Hybrid Approach Integrating Reinforcement Learning and Diffusion Models—0
CalibrationPhys: Self-supervised Video-based Heart and Respiratory Rate Measurements by Calibrating Between Multiple Cameras—0
Breaking the Curse of Dimensionality in Multiagent State Space: A Unified Agent Permutation Framework—0
Adversarial Feature Learning and Unsupervised Clustering based Speech Synthesis for Found Data with Acoustic and Textual Noise—0
Calibrated Diverse Ensemble Entropy Minimization for Robust Test-Time Adaptation in Prostate Cancer Detection—0
A Physics-based Generative Model to Synthesize Training Datasets for MRI-based Fat Quantification—0
A Continuous Mapping For Augmentation Design—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 2×112d) (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