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 3801–3850 of 8378 papers

TitleStatusHype
Implicit Rugosity Regularization via Data Augmentation—0
Adaptive Input-image Normalization for Solving the Mode Collapse Problem in GAN-based X-ray Images—0
From Traditional to Modern : Domain Adaptation for Action Classification in Short Social Video Clips—0
Handwritten Amharic Character Recognition Using a Convolutional Neural Network—0
From Style to Facts: Mapping the Boundaries of Knowledge Injection with Finetuning—0
Handwritten image augmentation—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
HardCore Generation: Generating Hard UNSAT Problems for Data Augmentation—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
From Overfitting to Robustness: Quantity, Quality, and Variety Oriented Negative Sample Selection in Graph Contrastive Learning—0
Hard-Synth: Synthesizing Diverse Hard Samples for ASR using Zero-Shot TTS and LLM—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
A Survey on Deep Clustering: From the Prior Perspective—0
From Human Mesenchymal Stromal Cells to Osteosarcoma Cells Classification by Deep Learning—0
From Fake to Hyperpartisan News Detection Using Domain Adaptation—0
Contrastive Learning as Goal-Conditioned Reinforcement Learning—0
From Dialect Gaps to Identity Maps: Tackling Variability in Speaker Verification—0
Harnessing The Power of Attention For Patch-Based Biomedical Image Classification—0
ContraGAN: Contrastive Learning for Conditional Image Generation—0
HARPT: A Corpus for Analyzing Consumers' Trust and Privacy Concerns in Mobile Health Apps—0
FRNET: Flattened Residual Network for Infant MRI Skull Stripping—0
HateGAN: Adversarial Generative-Based Data Augmentation for Hate Speech Detection—0
HATERecognizer at SemEval-2019 Task 5: Using Features and Neural Networks to Face Hate Recognition—0
Hate Speech Detection in Limited Data Contexts using Synthetic Data Generation—0
Contrastive Fine-tuning Improves Robustness for Neural Rankers—0
A Survey on Data Synthesis and Augmentation for Large Language Models—0
hdl2v: A Code Translation Dataset for Enhanced LLM Verilog Generation—0
Agriculture-Vision Challenge 2024 -- The Runner-Up Solution for Agricultural Pattern Recognition via Class Balancing and Model Ensemble—0
Adaptive Hybrid Masking Strategy for Privacy-Preserving Face Recognition Against Model Inversion Attack—0
A Car Model Identification System for Streamlining the Automobile Sales Process—0
2nd Place Solution for ICCV 2021 VIPriors Image Classification Challenge: An Attract-and-Repulse Learning Approach—0
Free Performance Gain from Mixing Multiple Partially Labeled Samples in Multi-label Image Classification—0
FreeAudio: Training-Free Timing Planning for Controllable Long-Form Text-to-Audio Generation—0
FRED: Towards a Full Rotation-Equivariance in Aerial Image Object Detection—0
A Survey on Data Augmentation for Text Classification—0
Heterogeneous Face Recognition via Face Synthesis with Identity-Attribute Disentanglement—0
FrAUG: A Frame Rate Based Data Augmentation Method for Depression Detection from Speech Signals—0
Heterogeneous Graph Contrastive Learning with Spectral Augmentation—0
Framework for lung CT image segmentation based on UNet++—0
Heterogeneous Recycle Generation for Chinese Grammatical Error Correction—0
Frame-level SpecAugment for Deep Convolutional Neural Networks in Hybrid ASR Systems—0
HiBid: A Cross-Channel Constrained Bidding System with Budget Allocation by Hierarchical Offline Deep Reinforcement Learning—0
Agriculture-Vision Challenge 2022 -- The Runner-Up Solution for Agricultural Pattern Recognition via Transformer-based Models—0
Fractal interpolation in the context of prediction accuracy optimization—0
Enhancing Face Recognition with Latent Space Data Augmentation and Facial Posture Reconstruction—0
FPMT: Enhanced Semi-Supervised Model for Traffic Incident Detection—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