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 3751–3800 of 8378 papers

TitleStatusHype
A Hybrid Approach for Learning to Shift and Grasp with Elaborate Motion Primitives—0
Adaptive Label Smoothing for Out-of-Distribution Detection—0
Graph Masked Autoencoder for Spatio-Temporal Graph Learning—0
Graph Mixup with Soft Alignments—0
Fully Automated Multi-Organ Segmentation in Abdominal Magnetic Resonance Imaging with Deep Neural Networks—0
Graph-Preserving Grid Layout: A Simple Graph Drawing Method for Graph Classification using CNNs—0
Fully automated deep learning based segmentation of normal, infarcted and edema regions from multiple cardiac MRI sequences—0
Full-Frame Scene Coordinate Regression for Image-Based Localization—0
Contrastive Learning is Just Meta-Learning—0
Contrastive Learning from Pairwise Measurements—0
GraphSR: A Data Augmentation Algorithm for Imbalanced Node Classification—0
Graph Structure and Feature Extrapolation for Out-of-Distribution Generalization—0
FSRT: Facial Scene Representation Transformer for Face Reenactment from Factorized Appearance Head-pose and Facial Expression Features—0
FSRT: Facial Scene Representation Transformer for Face Reenactment from Factorized Appearance, Head-pose, and Facial Expression Features—0
Contrastive Learning for Unsupervised Radar Place Recognition—0
A Survey on Face Data Augmentation—0
A Heterogeneous Graph Neural Network Fusing Functional and Structural Connectivity for MCI Diagnosis—0
FSDNet-An efficient fire detection network for complex scenarios based on YOLOv3 and DenseNet—0
Contrastive learning for unsupervised medical image clustering and reconstruction—0
FS-Depth: Focal-and-Scale Depth Estimation from a Single Image in Unseen Indoor Scene—0
Frustratingly Easy Natural Question Answering—0
Towards Learning (Dis)-Similarity of Source Code from Program Contrasts—0
Frozen-to-Paraffin: Categorization of Histological Frozen Sections by the Aid of Paraffin Sections and Generative Adversarial Networks—0
Frozen Feature Augmentation for Few-Shot Image Classification—0
FROTE: Feedback Rule-Driven Oversampling for Editing Models—0
Grouping-By-ID: Guarding Against Adversarial Domain Shifts—0
Contrastive Learning for Low Resource Machine Translation—0
GS-PT: Exploiting 3D Gaussian Splatting for Comprehensive Point Cloud Understanding via Self-supervised Learning—0
Implicit Rugosity Regularization via Data Augmentation—0
Adaptive Input-image Normalization for Solving the Mode Collapse Problem in GAN-based X-ray Images—0
GUESS: Generative Uncertainty Ensemble for Self Supervision—0
Guidance-Based Prompt Data Augmentation in Specialized Domains for Named Entity Recognition—0
From Traditional to Modern : Domain Adaptation for Action Classification in Short Social Video Clips—0
Guided Data Augmentation for Offline Reinforcement Learning and Imitation Learning—0
From Style to Facts: Mapping the Boundaries of Knowledge Injection with Finetuning—0
Guided Discrete Diffusion for Electronic Health Record Generation—0
From spoken dialogue to formal summary: An utterance rewriting for dialogue summarization—0
From Spelling to Grammar: A New Framework for Chinese Grammatical Error Correction—0
Contrastive Learning for Context-aware Neural Machine Translation Using Coreference Information—0
A Survey on Deep Domain Adaptation and Tiny Object Detection Challenges, Techniques and Datasets—0
From Reviews to Dialogues: Active Synthesis for Zero-Shot LLM-based Conversational Recommender System—0
HABD: a houma alliance book ancient handwritten character recognition database—0
From Overfitting to Robustness: Quantity, Quality, and Variety Oriented Negative Sample Selection in Graph Contrastive Learning—0
Hallucinations in neural machine translation—0
From Natural Language to SQL: Review of LLM-based Text-to-SQL Systems—0
Contrastive Learning for Context-aware Neural Machine TranslationUsing Coreference Information—0
HandDiffuse: Generative Controllers for Two-Hand Interactions via Diffusion Models—0
Hand gesture recognition using 802.11ad mmWave sensor in the mobile device—0
A Survey on Deep Clustering: From the Prior Perspective—0
From Human Mesenchymal Stromal Cells to Osteosarcoma Cells Classification by Deep Learning—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