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 7601–7650 of 8378 papers

TitleStatusHype
HateGAN: Adversarial Generative-Based Data Augmentation for Hate Speech Detection—0
HATERecognizer at SemEval-2019 Task 5: Using Features and Neural Networks to Face Hate Recognition—0
Hate Speech Detection in Limited Data Contexts using Synthetic Data Generation—0
HCS at SemEval-2017 Task 5: Polarity detection in business news using convolutional neural networks—0
hdl2v: A Code Translation Dataset for Enhanced LLM Verilog Generation—0
Heavy-tailed Representations, Text Polarity Classification & Data Augmentation—0
HEp-2 Cell Image Classification with Deep Convolutional Neural Networks—0
Heterogeneous Contrastive Learning: Encoding Spatial Information for Compact Visual Representations—0
Heterogeneous Face Recognition via Face Synthesis with Identity-Attribute Disentanglement—0
Heterogeneous Graph Contrastive Learning with Spectral Augmentation—0
Heterogeneous Recycle Generation for Chinese Grammatical Error Correction—0
HiBid: A Cross-Channel Constrained Bidding System with Budget Allocation by Hierarchical Offline Deep Reinforcement Learning—0
Hide and Seek: How Does Watermarking Impact Face Recognition?—0
Hierarchical Scene Coordinate Classification and Regression for Visual Localization—0
Hierarchical Neural Data Synthesis for Semantic Parsing—0
Hierarchical Topic Presence Models—0
High-Dimensional Regression with Gaussian Mixtures and Partially-Latent Response Variables—0
High-frequency shape recovery from shading by CNN and domain adaptation—0
Fusion Self-supervised Learning for Recommendation—0
High performing ensemble of convolutional neural networks for insect pest image detection—0
High-Quality Data Augmentation for Low-Resource NMT: Combining a Translation Memory, a GAN Generator, and Filtering—0
High-Resolution Mammogram Synthesis using Progressive Generative Adversarial Networks—0
High-Resolution UAV Image Generation for Sorghum Panicle Detection—0
HILGEN: Hierarchically-Informed Data Generation for Biomedical NER Using Knowledgebases and Large Language Models—0
HintedBT: Augmenting Back-Translation with Quality and Transliteration Hints—0
HIPODE: Enhancing Offline Reinforcement Learning with High-Quality Synthetic Data from a Policy-Decoupled Approach—0
HMM-based data augmentation for E2E systems for building conversational speech synthesis systems—0
HOGSA: Bimanual Hand-Object Interaction Understanding with 3D Gaussian Splatting Based Data Augmentation—0
Horses to Zebras: Ontology-Guided Data Augmentation and Synthesis for ICD-9 Coding—0
How Does Data Diversity Shape the Weight Landscape of Neural Networks?—0
How Does Frequency Bias Affect the Robustness of Neural Image Classifiers against Common Corruption and Adversarial Perturbations?—0
How Does Mixup Help With Robustness and Generalization?—0
How Effective is Task-Agnostic Data Augmentation for Pretrained Transformers?—0
How low is too low? A monolingual take on lemmatisation in Indian languages—0
How many labeled license plates are needed?—0
How Robust are Randomized Smoothing based Defenses to Data Poisoning?—0
How Tempering Fixes Data Augmentation in Bayesian Neural Networks—0
How to augment your ViTs? Consistency loss and StyleAug, a random style transfer augmentation—0
How to choose "Good" Samples for Text Data Augmentation—0
How to Make Neural Natural Language Generation as Reliable as Templates in Task-Oriented Dialogue—0
How to Select One Among All ? An Empirical Study Towards the Robustness of Knowledge Distillation in Natural Language Understanding—0
How to Tame Your Data: Data Augmentation for Dialog State Tracking—0
How to Train Your DRAGON: Diverse Augmentation Towards Generalizable Dense Retrieval—0
How we won BraTS 2023 Adult Glioma challenge? Just faking it! Enhanced Synthetic Data Augmentation and Model Ensemble for brain tumour segmentation—0
How Will It Drape Like? Capturing Fabric Mechanics from Depth Images—0
HpEIS: Learning Hand Pose Embeddings for Multimedia Interactive Systems—0
Human Age Estimation from Gene Expression Data using Artificial Neural Networks—0
Human-centered Interactive Learning via MLLMs for Text-to-Image Person Re-identification—0
Human Image Generation: A Comprehensive Survey—0
Human-in-the-loop online multi-agent approach to increase trustworthiness in ML models through trust scores and data augmentation—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