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 2401–2450 of 8378 papers

TitleStatusHype
Classification of Histopathological Biopsy Images Using Ensemble of Deep Learning Networks—0
Machine-learning-based head impact subtyping based on the spectral densities of the measurable head kinematics—0
A Fast and Robust BERT-based Dialogue State Tracker for Schema-Guided Dialogue Dataset—0
A Multilayered Block Network Model to Forecast Large Dynamic Transportation Graphs: an Application to US Air Transport—0
Effective LLM Knowledge Learning via Model Generalization—0
Effect of Random Histogram Equalization on Breast Calcification Analysis Using Deep Learning—0
Efficient Classification of Histopathology Images—0
Efficient, Lexicon-Free OCR using Deep Learning—0
Egocentric Gesture Recognition for Head-Mounted AR devices—0
End-to-End Speech-Translation with Knowledge Distillation: FBK@IWSLT2020—0
EPIC: Graph Augmentation with Edit Path Interpolation via Learnable Cost—0
Classification of complex local environments in systems of particle shapes through shape-symmetry encoded data augmentation—0
Classification Confidence Estimation with Test-Time Data-Augmentation—0
EffCNet: An Efficient CondenseNet for Image Classification on NXP BlueBox—0
A Robust Pose Transformational GAN for Pose Guided Person Image Synthesis—0
Classes Are Not Equal: An Empirical Study on Image Recognition Fairness—0
A Robust Illumination-Invariant Camera System for Agricultural Applications—0
Class-Based Time Series Data Augmentation to Mitigate Extreme Class Imbalance for Solar Flare Prediction—0
Class balanced underwater object detection dataset generated by class-wise style augmentation—0
Class-Aware Universum Inspired Re-Balance Learning for Long-Tailed Recognition—0
Class-Aware PillarMix: Can Mixed Sample Data Augmentation Enhance 3D Object Detection with Radar Point Clouds?—0
A Robust Ensemble Model for Patasitic Egg Detection and Classification—0
CLASP: Few-Shot Cross-Lingual Data Augmentation for Semantic Parsing—0
Clarifying Myths About the Relationship Between Shape Bias, Accuracy, and Robustness—0
A Robust Attack: Displacement Backdoor Attack—0
A robust assessment for invariant representations—0
Self-supervised New Activity Detection in Sensor-based Smart Environments—0
A Robust and Scalable Attention Guided Deep Learning Framework for Movement Quality Assessment—0
Aerial LaneNet: Lane Marking Semantic Segmentation in Aerial Imagery using Wavelet-Enhanced Cost-sensitive Symmetric Fully Convolutional Neural Networks—0
AdaAugment: A Tuning-Free and Adaptive Approach to Enhance Data Augmentation—0
Effective Data Augmentation Approaches to End-to-End Task-Oriented Dialogue—0
CKMDiff: A Generative Diffusion Model for CKM Construction via Inverse Problems with Learned Priors—0
CK4Gen: A Knowledge Distillation Framework for Generating High-Utility Synthetic Survival Datasets in Healthcare—0
ARMOR: Shielding Unlearnable Examples against Data Augmentation—0
ARMADA: Attribute-Based Multimodal Data Augmentation—0
CIPER: Combining Invariant and Equivariant Representations Using Contrastive and Predictive Learning—0
A Bayesian Approach to Invariant Deep Neural Networks—0
EduMT: Developing Machine Translation System for Educational Content in Indian Languages—0
EEGEncoder: Advancing BCI with Transformer-Based Motor Imagery Classification—0
CILDA: Contrastive Data Augmentation using Intermediate Layer Knowledge Distillation—0
Arithmetic Reasoning with LLM: Prolog Generation & Permutation—0
A Rigorous Evaluation of Real-World Distribution Shifts—0
Choreographing the Digital Canvas: A Machine Learning Approach to Artistic Performance—0
AEIOU: A Unified Defense Framework against NSFW Prompts in Text-to-Image Models—0
Edinburgh’s End-to-End Multilingual Speech Translation System for IWSLT 2021—0
Chinese Grammatical Error Correction Based on Hybrid Models with Data Augmentation—0
ADA: A Game-Theoretic Perspective on Data Augmentation for Object Detection—0
EDF: Ensemble, Distill, and Fuse for Easy Video Labeling—0
AEGR: A simple approach to gradient reversal in autoencoders for network anomaly detection—0
EDDA: Explanation-driven Data Augmentation to Improve Explanation Faithfulness—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