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.

Further readings:

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Papers

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Showing 4051–4100 of 8378 papers

TitleStatusHype
A Survey of Automated Data Augmentation Algorithms for Deep Learning-based Image Classification Tasks—0
Fine-Tuning Video Transformers for Word-Level Bangla Sign Language: A Comparative Analysis for Classification Tasks—0
Fine-Tuning Pre-trained Language Models for Robust Causal Representation Learning—0
Fine-tuning of Convolutional Neural Networks for the Recognition of Facial Expressions in Sign Language Video Samples—0
Improved Classification of White Blood Cells with the Generative Adversarial Network and Deep Convolutional Neural Network—0
Improved Consistency Training for Semi-Supervised Sequence-to-Sequence ASR via Speech Chain Reconstruction and Self-Transcribing—0
Finetuning Is a Surprisingly Effective Domain Adaptation Baseline in Handwriting Recognition—0
Improved Cotton Leaf Disease Classification Using Parameter-Efficient Deep Learning Framework—0
A supervised generative optimization approach for tabular data—0
Age Range Estimation using MTCNN and VGG-Face Model—0
Improved English to Hindi Multimodal Neural Machine Translation—0
Fine-Grained Sports, Yoga, and Dance Postures Recognition: A Benchmark Analysis—0
Fine-Grained Hard Negative Mining: Generalizing Mitosis Detection with a Fifth of the MIDOG 2022 Dataset—0
Improved Image-based Pose Regressor Models for Underwater Environments—0
Consistency and Monotonicity Regularization for Neural Knowledge Tracing—0
Improved Meta-Learning Training for Speaker Verification—0
Fine-Grained Few Shot Learning with Foreground Object Transformation—0
Consensus Clustering With Unsupervised Representation Learning—0
Fine-grained building roof instance segmentation based on domain adapted pretraining and composite dual-backbone—0
Fine-Grained Bias Detection in LLM: Enhancing detection mechanisms for nuanced biases—0
Consecutive Question Generation via Dynamic Multitask Learning—0
Improved Prosodic Clustering for Multispeaker and Speaker-independent Phoneme-level Prosody Control—0
Improved Recurrent Neural Networks for Session-based Recommendations—0
A Study on the Impact of Data Augmentation for Training Convolutional Neural Networks in the Presence of Noisy Labels—0
Training Robust Spiking Neural Networks on Neuromorphic Data with Spatiotemporal Fragments—0
Improved Regularization Techniques for End-to-End Speech Recognition—0
Improved Relation Classification by Deep Recurrent Neural Networks with Data Augmentation—0
Improved resistance of neural networks to adversarial images through generative pre-training—0
Age Prediction Performance Varies Across Deep, Superficial, and Cerebellar White Matter Connections—0
Improved singing voice separation with chromagram-based pitch-aware remixing—0
Adaptive Data Augmentation with Deep Parallel Generative Models—0
Improved Techniques For Weakly-Supervised Object Localization—0
Fine-Grained AutoAugmentation for Multi-Label Classification—0
CoNLL-SIGMORPHON 2017 Shared Task: Universal Morphological Reinflection in 52 Languages—0
Finding the Reflection Point: Unpadding Images to Remove Data Augmentation Artifacts in Large Open Source Image Datasets for Machine Learning—0
Findings of the Second Workshop on Neural Machine Translation and Generation—0
A study on joint modeling and data augmentation of multi-modalities for audio-visual scene classification—0
Improving 3D Object Detection through Progressive Population Based Augmentation—0
Finding NeMo: Negative-mined Mosaic Augmentation for Referring Image Segmentation—0
Improving Acoustic Scene Classification in Low-Resource Conditions—0
Finding and Fixing Spurious Patterns with Explanations—0
Improving age prediction: Utilizing LSTM-based dynamic forecasting for data augmentation in multivariate time series analysis—0
Improving Android Malware Detection Through Data Augmentation Using Wasserstein Generative Adversarial Networks—0
Improving Anomalous Sound Detection via Low-Rank Adaptation Fine-Tuning of Pre-Trained Audio Models—0
Financial Time Series Data Augmentation with Generative Adversarial Networks and Extended Intertemporal Return Plots—0
Conformer Based Elderly Speech Recognition System for Alzheimer's Disease Detection—0
A Study on FGSM Adversarial Training for Neural Retrieval—0
Finance document Extraction Using Data Augmentation and Attention—0
ConFit: Improving Resume-Job Matching using Data Augmentation and Contrastive Learning—0
Filling the Missing: Exploring Generative AI for Enhanced Federated Learning over Heterogeneous Mobile Edge Devices—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