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 4301–4350 of 8378 papers

TitleStatusHype
Domain Adaptive 3D Pose Augmentation for In-the-wild Human Mesh RecoveryCode1
Semantics-Depth-Symbiosis: Deeply Coupled Semi-Supervised Learning of Semantics and Depth—0
KnowDA: All-in-One Knowledge Mixture Model for Data Augmentation in Low-Resource NLP—0
Reconstruct from BEV: A 3D Lane Detection Approach based on Geometry Structure Prior—0
A ResNet attention model for classifying mosquitoes from wing‑beating soundsCode0
Reconstruct from Top View: A 3D Lane Detection Approach based on Geometry Structure Prior—0
Test-time image-to-image translation ensembling improves out-of-distribution generalization in histopathology—0
Visualizing and Understanding Contrastive LearningCode0
When Does Re-initialization Work?—0
Technical Report: Combining knowledge from Transfer Learning during training and Wide ResnetsCode0
Mitigating Data Heterogeneity in Federated Learning with Data AugmentationCode1
Test Time Transform Prediction for Open Set Histopathological Image RecognitionCode0
Transfer Learning for Robust Low-Resource Children's Speech ASR with Transformers and Source-Filter Warping—0
Data Augmentation vs. Equivariant Networks: A Theory of Generalization on Dynamics Forecasting—0
Hybrid Facial Expression Recognition (FER2013) Model for Real-Time Emotion Classification and Prediction—0
UI Layers Merger: Merging UI layers via Visual Learning and Boundary PriorCode0
Small Object Detection via Pixel Level Balancing With Applications to Blood Cell DetectionCode0
Explainable Global Error Weighted on Feature Importance: The xGEWFI metric to evaluate the error of data imputation and data augmentation—0
DU-Net based Unsupervised Contrastive Learning for Cancer Segmentation in Histology Images—0
VLMixer: Unpaired Vision-Language Pre-training via Cross-Modal CutMix—0
A Spatio-Temporal Neural Network Forecasting Approach for Emulation of Firefront Models—0
AI Enlightens Wireless Communication: A Transformer Backbone for CSI Feedback—0
Noisy Learning for Neural ODEs Acts as a Robustness Locus Widening—0
Longitudinal detection of new MS lesions using Deep Learning—0
PreCogIIITH at HinglishEval : Leveraging Code-Mixing Metrics & Language Model Embeddings To Estimate Code-Mix QualityCode0
NCAGC: A Neighborhood Contrast Framework for Attributed Graph ClusteringCode1
Simple-BEV: What Really Matters for Multi-Sensor BEV Perception?—0
iBoot: Image-bootstrapped Self-Supervised Video Representation Learning—0
MixGen: A New Multi-Modal Data AugmentationCode1
BaIT: Barometer for Information Trustworthiness—0
Real3D-Aug: Point Cloud Augmentation by Placing Real Objects with Occlusion Handling for 3D Detection and SegmentationCode1
Exploiting Cross-domain And Cross-Lingual Ultrasound Tongue Imaging Features For Elderly And Dysarthric Speech Recognition—0
RecBole 2.0: Towards a More Up-to-Date Recommendation LibraryCode4
Contrastive Learning as Goal-Conditioned Reinforcement Learning—0
A Projection-Based K-space Transformer Network for Undersampled Radial MRI Reconstruction with Limited Training Subjects—0
Physically-admissible polarimetric data augmentation for road-scene analysis—0
TriHorn-Net: A Model for Accurate Depth-Based 3D Hand Pose EstimationCode1
A Survey of Automated Data Augmentation Algorithms for Deep Learning-based Image Classification Tasks—0
Toward Student-Oriented Teacher Network Training For Knowledge Distillation—0
Mapping fNIRS to fMRI with Neural Data Augmentation and Machine Learning Models—0
Low-complexity deep learning frameworks for acoustic scene classification—0
Confident Sinkhorn Allocation for Pseudo-LabelingCode1
2nd Place Solution for ICCV 2021 VIPriors Image Classification Challenge: An Attract-and-Repulse Learning Approach—0
Virtual embeddings and self-consistency for self-supervised learning—0
GAN based Data Augmentation to Resolve Class Imbalance—0
Data Augmentation for Intent Classification—0
Modeling Generalized Specialist Approach To Train Quality Resilient Snapshot EnsembleCode0
The YiTrans End-to-End Speech Translation System for IWSLT 2022 Offline Shared Task—0
Memory Classifiers: Two-stage Classification for Robustness in Machine Learning—0
Lost in Transmission: On the Impact of Networking Corruptions on Video Machine Learning Models—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