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:

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Papers

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Showing 2101–2125 of 8378 papers

TitleStatusHype
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
DFlow: Diverse Dialogue Flow Simulation with Large Language Models—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
Developing efficient transfer learning strategies for robust scene recognition in mobile robotics using pre-trained convolutional neural networks—0
Developing neural machine translation models for Hungarian-English—0
DG2: Data Augmentation Through Document Grounded Dialogue Generation—0
Diabetes detection using deep learning techniques with oversampling and feature 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
Detection of Suicidal Risk on Social Media: A Hybrid Model—0
A supervised generative optimization approach for tabular data—0
Age Range Estimation using MTCNN and VGG-Face Model—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