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:

( Image credit: Albumentations )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 6376–6400 of 8378 papers

TitleStatusHype
Medical Scientific Table-to-Text Generation with Human-in-the-Loop under the Data Sparsity Constraint—0
Medication Mention Detection in Tweets Using ELECTRA Transformers and Decision Trees—0
Medication Regimen Extraction From Medical Conversations—0
MEDSAGE: Enhancing Robustness of Medical Dialogue Summarization to ASR Errors with LLM-generated Synthetic Dialogues—0
Melon Fruit Detection and Quality Assessment Using Generative AI-Based Image Data Augmentation—0
Membership-Doctor: Comprehensive Assessment of Membership Inference Against Machine Learning Models—0
Membership Privacy Evaluation in Deep Spiking Neural Networks—0
Memorize or Generalize? Evaluating LLM Code Generation with Evolved Questions—0
Memory-based Jitter: Improving Visual Recognition on Long-tailed Data with Diversity In Memory—0
Memory Classifiers: Two-stage Classification for Robustness in Machine Learning—0
"Mental Rotation" by Optimizing Transforming Distance—0
Social Media as an Instant Source of Feedback on Water Quality—0
Meta Approach to Data Augmentation Optimization—0
MetaAugment: Sample-Aware Data Augmentation Policy Learning—0
Meta Knowledge Distillation—0
Meta-Learning in Audio and Speech Processing: An End to End Comprehensive Review—0
Meta-Learning to Improve Pre-Training—0
MetaMind Neural Machine Translation System for WMT 2016—0
MetaMix: Towards Corruption-Robust Continual Learning With Temporally Self-Adaptive Data Transformation—0
MetaMixUp: Learning Adaptive Interpolation Policy of MixUp with Meta-Learning—0
MetaTool: Facilitating Large Language Models to Master Tools with Meta-task Augmentation—0
Meta-Transfer Derm-Diagnosis: Exploring Few-Shot Learning and Transfer Learning for Skin Disease Classification in Long-Tail Distribution—0
Meta-tuning Loss Functions and Data Augmentation for Few-shot Object Detection—0
SMILE: Speech Meta In-Context Learning for Low-Resource Language Automatic Speech Recognition—0
Method and Dataset Entity Mining in Scientific Literature: A CNN + Bi-LSTM Model with Self-attention—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