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 5651–5700 of 8378 papers

TitleStatusHype
Triplet Contrastive Learning for Brain Tumor Classification—0
MM-Mixing: Multi-Modal Mixing Alignment for 3D Understanding—0
Trustworthiness of Laser-Induced Breakdown Spectroscopy Predictions via Simulation-based Synthetic Data Augmentation and Multitask Learning—0
Trustworthy image-to-image translation: evaluating uncertainty calibration in unpaired training scenarios—0
Truth or Twist? Optimal Model Selection for Reliable Label Flipping Evaluation in LLM-based Counterfactuals—0
TSMind: Alibaba and Soochow University's Submission to the WMT22 Translation Suggestion Task—0
TSV Extrusion Morphology Classification Using Deep Convolutional Neural Networks—0
Tube-NeRF: Efficient Imitation Learning of Visuomotor Policies from MPC using Tube-Guided Data Augmentation and NeRFs—0
Tubule segmentation of fluorescence microscopy images based on convolutional neural networks with inhomogeneity correction—0
TULIP: Towards Unified Language-Image Pretraining—0
TumorNet: Lung Nodule Characterization Using Multi-View Convolutional Neural Network with Gaussian Process—0
TuneUp: A Simple Improved Training Strategy for Graph Neural Networks—0
Tuning Mixed Input Hyperparameters on the Fly for Efficient Population Based AutoRL—0
T\"upa at SemEval-2019 Task1: (Almost) feature-free Semantic Parsing—0
Twin Systems for DeepCBR: A Menagerie of Deep Learning and Case-Based Reasoning Pairings for Explanation and Data Augmentation—0
Twitter Data Augmentation for Monitoring Public Opinion on COVID-19 Intervention Measures—0
Two-Stage Augmentation and Adaptive CTC Fusion for Improved Robustness of Multi-Stream End-to-End ASR—0
Two-stage Cytopathological Image Synthesis for Augmenting Cervical Abnormality Screening—0
Two-Staged Acoustic Modeling Adaption for Robust Speech Recognition by the Example of German Oral History Interviews—0
Two Stream Networks for Self-Supervised Ego-Motion Estimation—0
TWR-MCAE: A Data Augmentation Method for Through-the-Wall Radar Human Motion Recognition—0
U2++: Unified Two-pass Bidirectional End-to-end Model for Speech Recognition—0
UA-KO at SemEval-2022 Task 11: Data Augmentation and Ensembles for Korean Named Entity Recognition—0
UAVs and Neural Networks for search and rescue missions—0
UAV-Sim: NeRF-based Synthetic Data Generation for UAV-based Perception—0
UAVTwin: Neural Digital Twins for UAVs using Gaussian Splatting—0
UDRN: Unified Dimensional Reduction Neural Network for Feature Selection and Feature Projection—0
UFRC: A Unified Framework for Reliable COVID-19 Detection on Crowdsourced Cough Audio—0
UI Layers Group Detector: Grouping UI Layers via Text Fusion and Box Attention—0
Ulcerative Colitis Mayo Endoscopic Scoring Classification with Active Learning and Generative Data Augmentation—0
Ultrafast-and-Ultralight ConvNet-Based Intelligent Monitoring System for Diagnosing Early-Stage Mpox Anytime and Anywhere—0
UMass PCL at SemEval-2022 Task 4: Pre-trained Language Model Ensembles for Detecting Patronizing and Condescending Language—0
Unbiased Faster R-CNN for Single-source Domain Generalized Object Detection—0
Uncertainty Estimation and Out-of-Distribution Detection for Deep Learning-Based Image Reconstruction using the Local Lipschitz—0
Unconstrained Road Marking Recognition with Generative Adversarial Networks—0
Undersensitivity in Neural Reading Comprehension—0
Understanding Adversarial Robustness Through Loss Landscape Geometries—0
Gradient-Leaks: Understanding and Controlling Deanonymization in Federated Learning—0
Understanding and Improving Robustness of Vision Transformers through Patch-based Negative Augmentation—0
Understanding and Mitigating Memorization in Diffusion Models for Tabular Data—0
Understanding and Mitigating the Bias Inheritance in LLM-based Data Augmentation on Downstream Tasks—0
Understanding Augmentation-based Self-Supervised Representation Learning via RKHS Approximation and Regression—0
On the Calibration of Multilingual Question Answering LLMs—0
Understanding Community Bias Amplification in Graph Representation Learning—0
Understanding data augmentation for classification: when to warp?—0
Understanding Data Augmentation from a Robustness Perspective—0
Understanding Data Augmentation in Neural Machine Translation: Two Perspectives towards Generalization—0
PAC-Chernoff Bounds: Understanding Generalization in the Interpolation Regime—0
Understanding Learning Invariance in Deep Linear Networks—0
Understanding Masked Autoencoders From a Local Contrastive Perspective—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