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 5201–5250 of 8378 papers

TitleStatusHype
YOLOv5s-GTB: light-weighted and improved YOLOv5s for bridge crack detection—0
Integrating Prior Knowledge in Contrastive Learning with KernelCode0
Long-tailed Recognition by Learning from Latent Categories—0
eRock at Qur’an QA 2022: Contemporary Deep Neural Networks for Qur’an based Reading Comprehension Question Answers—0
Exploring Text Recombination for Automatic Narrative Level Detection—0
Order-sensitive Shapley Values for Evaluating Conceptual Soundness of NLP Models—0
Exploring Data Augmentation Strategies for Hate Speech Detection in Roman Urdu—0
Mitigating Dataset Artifacts in Natural Language Inference Through Automatic Contextual Data Augmentation and Learning Optimization—0
Automatic Gloss-level Data Augmentation for Sign Language Translation—0
Speech Data Augmentation for Improving Phoneme Transcriptions of Aphasic Speech Using Wav2Vec 2.0 for the PSST Challenge—0
Fine-tuning of Convolutional Neural Networks for the Recognition of Facial Expressions in Sign Language Video Samples—0
Efficient Scheduling of Data Augmentation for Deep Reinforcement Learning—0
Glyph Features Matter: A Multimodal Solution for EvaHan in LT4HALA2022—0
Rethinking the Augmentation Module in Contrastive Learning: Learning Hierarchical Augmentation Invariance with Expanded ViewsCode0
An Inflectional Database for GitksanCode0
Data Augmentation for Low-resource Word Segmentation and POS Tagging of Ancient Chinese Texts—0
LuxemBERT: Simple and Practical Data Augmentation in Language Model Pre-Training for Luxembourgish—0
Examining the Effects of Language-and-Vision Data Augmentation for Generation of Descriptions of Human Faces—0
Towards Generalisable Audio Representations for Audio-Visual Navigation—0
Data Expansion Using WordNet-based Semantic Expansion and Word Disambiguation for Cyberbullying Detection—0
Data Augmentation for the Post-Stroke Speech Transcription (PSST) Challenge: Sometimes Less Is More—0
Effectiveness of Data Augmentation and Pretraining for Improving Neural Headline Generation in Low-Resource Settings—0
Ancient Chinese Word Segmentation and Part-of-Speech Tagging Using Data Augmentation—0
A First Attempt at Unreliable News Detection in Swedish—0
Scaling up Discourse Quality Annotation for Political ScienceCode0
Self-supervised Learning for Label Sparsity in Computational Drug Repositioning—0
Point-Teaching: Weakly Semi-Supervised Object Detection with Point Annotations—0
Tackling Irony Detection using Ensemble ClassifiersCode0
A Multi-level Supervised Contrastive Learning Framework for Low-Resource Natural Language Inference—0
Augmentation-Aware Self-Supervision for Data-Efficient GAN TrainingCode0
A Kernelised Stein Statistic for Assessing Implicit Generative ModelsCode0
Your Contrastive Learning Is Secretly Doing Stochastic Neighbor EmbeddingCode0
Duplex Conversation: Towards Human-like Interaction in Spoken Dialogue Systems—0
Segmentation Consistency Training: Out-of-Distribution Generalization for Medical Image SegmentationCode0
Adversarial synthesis based data-augmentation for code-switched spoken language identification—0
Graph Structure Based Data Augmentation Method—0
A General Multiple Data Augmentation Based Framework for Training Deep Neural Networks—0
Saliency Map Based Data Augmentation—0
MDMLP: Image Classification from Scratch on Small Datasets with MLPCode0
Who is we? Disambiguating the referents of first person plural pronouns in parliamentary debates—0
How Tempering Fixes Data Augmentation in Bayesian Neural Networks—0
Leveraging Causal Inference for Explainable Automatic Program Repair—0
Triangular Contrastive Learning on Molecular Graphs—0
Audio Data Augmentation for Acoustic-to-articulatory Speech Inversion using Bidirectional Gated RNNs—0
Counterfactual Data Augmentation improves Factuality of Abstractive Summarization—0
An Empirical Study on Distribution Shift Robustness From the Perspective of Pre-Training and Data Augmentation—0
Leveraging QA Datasets to Improve Generative Data AugmentationCode0
Conditional set generation using Seq2seq models—0
Augmentation-induced Consistency Regularization for Classification—0
Does Your Model Classify Entities Reasonably? Diagnosing and Mitigating Spurious Correlations in Entity TypingCode0
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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