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 6501–6550 of 8378 papers

TitleStatusHype
Data Augmentation for Neural Online Chats Response Selection—0
Data Augmentation for Opcode Sequence Based Malware Detection—0
Data Augmentation for Personal Knowledge Base Population—0
Data Augmentation for Rare Symptoms in Vaccine Side-Effect Detection—0
Data augmentation and refinement for recommender system: A semi-supervised approach using maximum margin matrix factorization—0
Data Augmentation for Robust Keyword Spotting under Playback Interference—0
Data Augmentation for Rumor Detection Using Context-Sensitive Neural Language Model With Large-Scale Credibility Corpus—0
Data Augmentation for Sample Efficient and Robust Document Ranking—0
Data Augmentation for Seizure Prediction with Generative Diffusion Model—0
Data Augmentation for Sign Language Gloss Translation—0
Data Augmentation for Skin Lesion using Self-Attention based Progressive Generative Adversarial Network—0
Data Augmentation For Small Object using Fast AutoAugment—0
Analysis of Data Augmentation Methods for Low-Resource Maltese ASR—0
Data Augmentation for Spoken Language Understanding via Joint Variational Generation—0
Data Augmentation for Text-based Person Retrieval Using Large Language Models—0
Data Augmentation for Text Generation Without Any Augmented Data—0
Data Augmentation for the Post-Stroke Speech Transcription (PSST) Challenge: Sometimes Less Is More—0
Data Augmentation for Time-Series Classification: An Extensive Empirical Study and Comprehensive Survey—0
Data Augmentation for Traffic Classification—0
Data Augmentation for Training Dialog Models Robust to Speech Recognition Errors—0
Data Augmentation for Transformer-based G2P—0
Data Augmentation for Visual Question Answering—0
Data Augmentation for Voice-Assistant NLU using BERT-based Interchangeable Rephrase—0
Data Augmentation Imbalance For Imbalanced Attribute Classification—0
Data augmentation in Bayesian neural networks and the cold posterior effect—0
Data Augmentation in Earth Observation: A Diffusion Model Approach—0
Data Augmentation in Emotion Classification Using Generative Adversarial Networks—0
Data Augmentation in Graph Neural Networks: The Role of Generated Synthetic Graphs—0
Data Augmentation in Human-Centric Vision—0
Data Augmentation in Natural Language Processing: A Novel Text Generation Approach for Long and Short Text Classifiers—0
Data Augmentation Integrating Dialogue Flow and Style to Adapt Spoken Dialogue Systems to Low-Resource User Groups—0
Data Augmentation in Time Series Forecasting through Inverted Framework—0
Data Augmentation in Training CNNs: Injecting Noise to Images—0
Data Augmentation in Training CNNs: Injecting Noise to Images—0
On Evaluation Protocols for Data Augmentation in a Limited Data Scenario—0
Data augmentation method for modeling health records with applications to clopidogrel treatment failure detection—0
Data Augmentation Methods for Anaphoric Zero Pronouns—0
Data Augmentation Methods for End-to-end Speech Recognition on Distant-Talk Scenarios—0
Data Augmentation Methods of Dynamic Model Identification for Harbor Maneuvers using Feedforward Neural Network—0
Data Augmentation Method Utilizing Template Sentences for Variable Definition Extraction—0
Data Augmentation of Contrastive Learning is Estimating Positive-incentive Noise—0
Data Augmentation of IMU Signals and Evaluation via a Semi-Supervised Classification of Driving Behavior—0
Data Augmentation of Incorporating Real Error Patterns and Linguistic Knowledge for Grammatical Error Correction—0
Data Augmentation of Multi-turn Psychological Dialogue via Knowledge-driven Progressive Thought Prompting—0
Data Augmentation of Multivariate Sensor Time Series using Autoregressive Models and Application to Failure Prognostics—0
Data Augmentation of Railway Images for Track Inspection—0
Data Augmentation of Time-Series Data in Human Movement Biomechanics: A Scoping Review—0
Data Augmentation Policy Search for Long-Term Forecasting—0
Data Augmentation Revisited: Rethinking the Distribution Gap between Clean and Augmented Data—0
Data Augmentation Scheme for Raman Spectra with Highly Correlated Annotations—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