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 2101–2150 of 8378 papers

TitleStatusHype
DiffAutoML: Differentiable Joint Optimization for Efficient End-to-End Automated Machine Learning—0
Context-Preserving Instance-Level Augmentation and Deformable Convolution Networks for SAR Ship Detection—0
Adaptive Few-Shot Learning (AFSL): Tackling Data Scarcity with Stability, Robustness, and Versatility—0
A Survey of Methods for Addressing Class Imbalance in Deep-Learning Based Natural Language Processing—0
Context-gloss Augmentation for Improving Word Sense Disambiguation—0
Aggrotech: Leveraging Deep Learning for Sustainable Tomato Disease Management—0
Context-Aware Language Modeling for Goal-Oriented Dialogue Systems—0
A Survey of Large Language Models for Financial Applications: Progress, Prospects and Challenges—0
VITAL: Interactive Few-Shot Imitation Learning via Visual Human-in-the-Loop Corrections—0
DiffECG: A Versatile Probabilistic Diffusion Model for ECG Signals Synthesis—0
Context-Aware Data Augmentation for LIDAR 3D Object Detection—0
Context-Aware Attention-Based Data Augmentation for POI Recommendation—0
Context-aware Adversarial Training for Name Regularity Bias in Named Entity Recognition—0
A Survey of GPT-3 Family Large Language Models Including ChatGPT and GPT-4—0
Adaptive Feature Selection for End-to-End Speech Translation—0
Content-Conditioned Generation of Stylized Free hand Sketches—0
CONTEMPLATING REAL-WORLDOBJECT RECOGNITION—0
Aggression Detection in Social Media: Using Deep Neural Networks, Data Augmentation, and Pseudo Labeling—0
HPCTransCompile: An AI Compiler Generated Dataset for High-Performance CUDA Transpilation and LLM Preliminary Exploration—0
Diff-2-in-1: Bridging Generation and Dense Perception with Diffusion Models—0
Cap2Aug: Caption guided Image to Image data Augmentation—0
A Survey of Automated Data Augmentation Algorithms for Deep Learning-based Image Classification Tasks—0
Consistent Text Categorization using Data Augmentation in e-Commerce—0
A supervised generative optimization approach for tabular data—0
Age Range Estimation using MTCNN and VGG-Face Model—0
DiCOVA-Net: Diagnosing COVID-19 using Acoustics based on Deep Residual Network for the DiCOVA Challenge 2021—0
Consistency and Monotonicity Regularization for Neural Knowledge Tracing—0
Age Prediction Performance Varies Across Deep, Superficial, and Cerebellar White Matter Connections—0
Consensus Clustering With Unsupervised Representation Learning—0
Consecutive Question Generation via Dynamic Multitask Learning—0
A Study on the Impact of Data Augmentation for Training Convolutional Neural Networks in the Presence of Noisy Labels—0
Adaptive Data Augmentation with Deep Parallel Generative Models—0
Dictionary-based Data Augmentation for Cross-Domain Neural Machine Translation—0
CoNLL-SIGMORPHON 2017 Shared Task: Universal Morphological Reinflection in 52 Languages—0
A study on joint modeling and data augmentation of multi-modalities for audio-visual scene classification—0
Adaptive Data Augmentation on Temporal Graphs—0
Conformer Based Elderly Speech Recognition System for Alzheimer's Disease Detection—0
A Study on FGSM Adversarial Training for Neural Retrieval—0
Abutting Grating Illusion: Cognitive Challenge to Neural Network Models—0
ConFit: Improving Resume-Job Matching using Data Augmentation and Contrastive Learning—0
Configuring Data Augmentations to Reduce Variance Shift in Positional Embedding of Vision Transformers—0
A study on cross-corpus speech emotion recognition and data augmentation—0
Confidence-Guided Data Augmentation for Improved Semi-Supervised Training—0
A Study of Unsupervised Evaluation Metrics for Practical and Automatic Domain Adaptation—0
A Generative Neural Annealer for Black-Box Combinatorial Optimization—0
Unified Framework for Histopathology Image Augmentation and Classification via Generative Models—0
A Study of Transfer Learning in Music Source Separation—0
Conditional Synthetic Food Image Generation—0
Conditional Synthetic Data Generation for Robust Machine Learning Applications with Limited Pandemic Data—0
A study of the impact of generative AI-based data augmentation on software metadata classification—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 2×96d) (Faster AA)Percentage error2—Unverified
5Shake-Shake (26 2×112d) (Faster AA)Percentage error2—Unverified
#ModelMetricClaimedVerifiedStatus
1DiffAugClassification Accuracy92.7—Unverified
2PaCMAPClassification Accuracy85.3—Unverified
3hNNEClassification Accuracy77.4—Unverified
4TopoAEClassification Accuracy74.6—Unverified