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 1701–1750 of 8378 papers

TitleStatusHype
Data Augmentation Policy Search for Long-Term Forecasting—0
A Lottery Ticket Hypothesis Framework for Low-Complexity Device-Robust Neural Acoustic Scene Classification—0
Augmentation Learning for Semi-Supervised Classification—0
Accenture at CheckThat! 2021: Interesting claim identification and ranking with contextually sensitive lexical training data augmentation—0
Augmentation Invariant Manifold Learning—0
Augmentation Inside the Network—0
3D CNN-based classification using sMRI and MD-DTI images for Alzheimer disease studies—0
Data Augmentation Revisited: Rethinking the Distribution Gap between Clean and Augmented Data—0
Augmentation-induced Consistency Regularization for Classification—0
A Localisation-Segmentation Approach for Multi-label Annotation of Lumbar Vertebrae using Deep Nets—0
Augmentation-based Unsupervised Cross-Domain Functional MRI Adaptation for Major Depressive Disorder Identification—0
All-Weather Object Recognition Using Radar and Infrared Sensing—0
A data augmentation strategy for deep neural networks with application to epidemic modelling—0
Augmentation-Based Deep Learning for Identification of Circulating Tumor Cells—0
A Data Augmentation Pipeline to Generate Synthetic Labeled Datasets of 3D Echocardiography Images using a GAN—0
Trainable Pointwise Decoder Module for Point Cloud Segmentation—0
A limited-size ensemble of homogeneous CNN/LSTMs for high-performance word classification—0
Augment-and-Conquer Negative Binomial Processes—0
Accent conversion using discrete units with parallel data synthesized from controllable accented TTS—0
Data Augmentation of Time-Series Data in Human Movement Biomechanics: A Scoping Review—0
Data Augmentation Scheme for Raman Spectra with Highly Correlated Annotations—0
AlignMix: Improving representations by interpolating aligned features—0
AugLoss: A Robust Augmentation-based Fine Tuning Methodology—0
Image retrieval outperforms diffusion models on data augmentation—0
AugInsert: Learning Robust Visual-Force Policies via Data Augmentation for Object Assembly Tasks—0
AugGAN: Cross Domain Adaptation with GAN-based Data Augmentation—0
Unsupervised Large Language Model Alignment for Information Retrieval via Contrastive Feedback—0
Towards Practical Few-shot Federated NLP—0
A data augmentation methodology for training machine/deep learning gait recognition algorithms—0
Acceleration of Grokking in Learning Arithmetic Operations via Kolmogorov-Arnold Representation—0
A Data Augmentation Method for Fully Automatic Brain Tumor Segmentation—0
Data Augmentation of Incorporating Real Error Patterns and Linguistic Knowledge for Grammatical Error Correction—0
Aug2Search: Enhancing Facebook Marketplace Search with LLM-Generated Synthetic Data Augmentation—0
3D Brain MRI Classification for Alzheimer Diagnosis Using CNN with Data Augmentation—0
Data Augmentation of Multi-turn Psychological Dialogue via Knowledge-driven Progressive Thought Prompting—0
Auditory-Based Data Augmentation for End-to-End Automatic Speech Recognition—0
Audio-visual scene classification: analysis of DCASE 2021 Challenge submissions—0
Data augmentation by morphological mixup for solving Raven's Progressive Matrices—0
Audio-to-Audio Emotion Conversion With Pitch And Duration Style Transfer—0
AudioSpa: Spatializing Sound Events with Text—0
A lightweight network for photovoltaic cell defect detection in electroluminescence images based on neural architecture search and knowledge distillation—0
Accelerating Representation Learning with View-Consistent Dynamics in Data-Efficient Reinforcement Learning—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 Augmentations in Deep Weight Spaces—0
A Lightweight Multi Aspect Controlled Text Generation Solution For Large Language Models—0
Audio Denoising for Robust Audio Fingerprinting—0
Accelerating Real-Time Question Answering via Question Generation—0
Audio Defect Detection in Music with Deep Networks—0
Audio Data Augmentation for Acoustic-to-articulatory Speech Inversion using Bidirectional Gated RNNs—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