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 3801–3825 of 8378 papers

TitleStatusHype
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
Handwritten Amharic Character Recognition Using a Convolutional Neural Network—0
From Reviews to Dialogues: Active Synthesis for Zero-Shot LLM-based Conversational Recommender System—0
Handwritten image augmentation—0
From Overfitting to Robustness: Quantity, Quality, and Variety Oriented Negative Sample Selection in Graph Contrastive Learning—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
HardCore Generation: Generating Hard UNSAT Problems for Data Augmentation—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
Hard-Synth: Synthesizing Diverse Hard Samples for ASR using Zero-Shot TTS and LLM—0
Contrastive Learning as Goal-Conditioned Reinforcement Learning—0
From Dialect Gaps to Identity Maps: Tackling Variability in Speaker Verification—0
ContraGAN: Contrastive Learning for Conditional Image Generation—0
FRNET: Flattened Residual Network for Infant MRI Skull Stripping—0
Contrastive Fine-tuning Improves Robustness for Neural Rankers—0
A Survey on Data Synthesis and Augmentation for Large Language Models—0
Agriculture-Vision Challenge 2024 -- The Runner-Up Solution for Agricultural Pattern Recognition via Class Balancing and Model Ensemble—0
Harnessing The Power of Attention For Patch-Based Biomedical Image Classification—0
Adaptive Hybrid Masking Strategy for Privacy-Preserving Face Recognition Against Model Inversion Attack—0
HARPT: A Corpus for Analyzing Consumers' Trust and Privacy Concerns in Mobile Health Apps—0
A Car Model Identification System for Streamlining the Automobile Sales Process—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