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 4101–4150 of 8378 papers

TitleStatusHype
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
Fighting Fire with Fire: Avoiding DNN Shortcuts through Priming—0
FIESTA: Fourier-Based Semantic Augmentation with Uncertainty Guidance for Enhanced Domain Generalizability in Medical Image Segmentation—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
Adaptive Data Augmentation on Temporal Graphs—0
Field-of-View IoU for Object Detection in 360° Images—0
FiE: Building a Global Probability Space by Leveraging Early Fusion in Encoder for Open-Domain Question Answering—0
Few-shot Weakly-supervised Cybersecurity Anomaly Detection—0
Unified Framework for Histopathology Image Augmentation and Classification via Generative Models—0
A Study of Transfer Learning in Music Source Separation—0
Few-Shot Object Detection in Real Life: Case Study on Auto-Harvest—0
Conditional Synthetic Food Image Generation—0
Few-Shot Natural Language Inference Generation with PDD: Prompt and Dynamic Demonstration—0
Few-shot Mining of Naturally Occurring Inputs and Outputs—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
A Generative Model to Synthesize EEG Data for Epileptic Seizure Prediction—0
Few-Shot Learning with Adaptive Weight Masking in Conditional GANs—0
Conditional set generation using Seq2seq models—0
Few-shot Learning using Data Augmentation and Time-Frequency Transformation for Time Series Classification—0
Conditional Semi-Supervised Data Augmentation for Spam Message Detection with Low Resource Data—0
Few-shot Hate Speech Detection Based on the MindSpore Framework—0
Few-Shot Generation of Brain Tumors for Secure and Fair Data Sharing—0
Few-Shot Defect Segmentation Leveraging Abundant Normal Training Samples Through Normal Background Regularization and Crop-and-Paste Operation—0
Conditionally-Conjugate Gaussian Process Factor Analysis for Spike Count Data via Data Augmentation—0
A Study of Enhancement, Augmentation, and Autoencoder Methods for Domain Adaptation in Distant Speech Recognition—0
Adaptive Data Augmentation for Thompson Sampling—0
Conditional Generative Data Augmentation for Clinical Audio Datasets—0
Conditional Generative Adversarial Networks for Data Augmentation and Adaptation in Remotely Sensed Imagery—0
A Study of Data Augmentation Techniques to Overcome Data Scarcity in Wound Classification using Deep Learning—0
Few-shot Class-incremental Learning for Cross-domain Disease Classification—0
Few-shot brain segmentation from weakly labeled data with deep heteroscedastic multi-task networks—0
Conditional Generation of Synthetic Geospatial Images from Pixel-level and Feature-level Inputs—0
Ferrograph image classification—0
Conditional Generation of Medical Images via Disentangled Adversarial Inference—0
A Study of Augmentation Methods for Handwritten Stenography Recognition—0
FenceMask: A Data Augmentation Approach for Pre-extracted Image Features—0
Federated Semi-supervised Learning for Medical Image Segmentation with intra-client and inter-client Consistency—0
Federated Self-supervised Domain Generalization for Label-efficient Polyp Segmentation—0
Conditional Augmentation for Generative Modeling—0
A General Multiple Data Augmentation Based Framework for Training Deep Neural Networks—0
Adaptive Data Augmentation for Contrastive Learning—0
Abutting Grating Illusion: Cognitive Challenge to Neural Network Models—0
Abstract Text Summarization: A Low Resource Challenge—0
HPCTransCompile: An AI Compiler Generated Dataset for High-Performance CUDA Transpilation and LLM Preliminary Exploration—0
Federated Learning for Traffic Flow Prediction with Synthetic Data Augmentation—0
Conditional Augmentation for Aspect Term Extraction via Masked Sequence-to-Sequence Generation—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