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 1601–1650 of 8378 papers

TitleStatusHype
A Unified Contrastive Transfer Framework with Propagation Structure for Boosting Low-Resource Rumor Detection—0
The Causal Structure of Domain Invariant Supervised Representation Learning—0
Grading and Anomaly Detection for Automated Retinal Image Analysis using Deep Learning—0
A U-Net Based Discriminator for Generative Adversarial Networks—0
A multi-category inverse design neural network and its application to diblock copolymers—0
Data Augmentation for Low-Resource Dialogue Summarization—0
AUGVIC: Exploiting BiText Vicinity for Low-Resource NMT—0
A Multi-cascaded Model with Data Augmentation for Enhanced Paraphrase Detection in Short Texts—0
Accurate Face Detection for High Performance—0
Amsqr at SemEval-2022 Task 4: Towards AutoNLP via Meta-Learning and Adversarial Data Augmentation for PCL Detection—0
Label Augmentation for Neural Networks Robustness—0
AugMixCloak: A Defense against Membership Inference Attacks via Image Transformation—0
Additional Look into GAN-based Augmentation for Deep Learning COVID-19 Image Classification—0
3D Data Augmentation for Driving Scenes on Camera—0
Data Augmentation for Low-Resource Dialogue Summarization—0
Data augmentation for low-resource grapheme-to-phoneme mapping—0
AugmentTRAJ: A framework for point-based trajectory data augmentation—0
Adding Instructions during Pretraining: Effective Way of Controlling Toxicity in Language Models—0
Data Augmentation for Intent Classification of German Conversational Agents in the Finance Domain—0
A Morphologically-Aware Dictionary-based Data Augmentation Technique for Machine Translation of Under-Represented Languages—0
Accurate and Diverse LLM Mathematical Reasoning via Automated PRM-Guided GFlowNets—0
Data Augmentation For Label Enhancement—0
ADD: Attribution-Driven Data Augmentation Framework for Boosting Image Super-Resolution—0
Accurate and fast identification of minimally prepared bacteria phenotypes using Raman spectroscopy assisted by machine learning—0
Augment on Manifold: Mixup Regularization with UMAP—0
Augmenting Vision-Based Human Pose Estimation with Rotation Matrix—0
A Model of Two Tales: Dual Transfer Learning Framework for Improved Long-tail Item Recommendation—0
Data Augmentation for Intent Classification—0
Data Augmentation for Leaf Segmentation and Counting Tasks in Rosette Plants—0
Augmenting transferred representations for stock classification—0
A Model Generalization Study in Localizing Indoor Cows with COw LOcalization (COLO) dataset—0
Augmenting Small Data to Classify Contextualized Dialogue Acts for Exploratory Visualization—0
A Mobile Food Recognition System for Dietary Assessment—0
AdBooster: Personalized Ad Creative Generation using Stable Diffusion Outpainting—0
Data Augmentation for Intent Classification with Generic Large Language Models—0
A Method of Data Augmentation to Train a Small Area Fingerprint Recognition Deep Neural Network with a Normal Fingerprint Database—0
Augmenting Radio Signals with Wavelet Transform for Deep Learning-Based Modulation Recognition—0
Augmenting NLP models using Latent Feature Interpolations—0
Adaptively Weighted Data Augmentation Consistency Regularization for Robust Optimization under Concept Shift—0
Data Augmentation with Unsupervised Machine Translation Improves the Structural Similarity of Cross-lingual Word Embeddings—0
Augmenting NLP data to counter Annotation Artifacts for NLI Tasks—0
Augmenting Medical Imaging: A Comprehensive Catalogue of 65 Techniques for Enhanced Data Analysis—0
A Meta Understanding of Meta-Learning—0
Augmenting learning using symmetry in a biologically-inspired domain—0
Augmenting Imitation Experience via Equivariant Representations—0
A Meta Approach to Defend Noisy Labels by the Manifold Regularizer PSDR—0
AdaTransform: Adaptive Data Transformation—0
Augmenting Image Question Answering Dataset by Exploiting Image Captions—0
Augmenting Ground-Level PM2.5 Prediction via Kriging-Based Pseudo-Label Generation—0
3D Convolutional Neural Networks for Efficient and Robust Hand Pose Estimation From Single Depth Images—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