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 5001–5050 of 8378 papers

TitleStatusHype
Untargeted White-box Adversarial Attack with Heuristic Defence Methods in Real-time Deep Learning based Network Intrusion Detection System—0
Untie the Knots: An Efficient Data Augmentation Strategy for Long-Context Pre-Training in Language Models—0
Unveiling Causalities in SAR ATR: A Causal Interventional Approach for Limited Data—0
Unveiling Gender Bias in Terms of Profession Across LLMs: Analyzing and Addressing Sociological Implications—0
UoB at ProfNER 2021: Data Augmentation for Classification Using Machine Translation—0
UPB @ ACTI: Detecting Conspiracies using fine tuned Sentence Transformers—0
A Note on Generalization in Variational Autoencoders: How Effective Is Synthetic Data & Overparameterization?—0
UrbanCAD: Towards Highly Controllable and Photorealistic 3D Vehicles for Urban Scene Simulation—0
Urban-Focused Multi-Task Offline Reinforcement Learning with Contrastive Data Sharing—0
Urban Scene Semantic Segmentation with Low-Cost Coarse Annotation—0
A Permuted Autoregressive Approach to Word-Level Recognition for Urdu Digital Text—0
URRL-IMVC: Unified and Robust Representation Learning for Incomplete Multi-View Clustering—0
USegMix: Unsupervised Segment Mix for Efficient Data Augmentation in Pathology Images—0
User lung cancer classification using efficientnet from ct scan images—0
Using a one-dimensional convolutional neural network with a conditional generative adversarial network to classify plant electrical signals—0
Using Artificial Intelligence for the Automation of Knitting Patterns—0
Using Data Augmentations and VTLN to Reduce Bias in Dutch End-to-End Speech Recognition Systems—0
Using Deep Mixture-of-Experts to Detect Word Meaning Shift for TempoWiC—0
Using GPT-4 to Augment Unbalanced Data for Automatic Scoring—0
Using Knowledge Distillation to improve interpretable models in a retail banking context—0
Using Large Language Models to Provide Explanatory Feedback to Human Tutors—0
Using Multi-scale SwinTransformer-HTC with Data augmentation in CoNIC Challenge—0
Using Out-of-the-Box Frameworks for Contrastive Unpaired Image Translation for Vestibular Schwannoma and Cochlea Segmentation: An approach for the crossMoDA Challenge—0
Using Paraphrasing and Memory-Augmented Models to Combat Data Sparsity in Question Interpretation with a Virtual Patient Dialogue System—0
Using Person Embedding to Enrich Features and Data Augmentation for Classification—0
Using Synthetic Data for Conversational Response Generation in Low-resource Settings—0
Using Test-Time Data Augmentation for Cross-Domain Atrial Fibrillation Detection from ECG Signals—0
Using U-Net Network for Efficient Brain Tumor Segmentation in MRI Images—0
Using Weak Supervision and Data Augmentation in Question Answering—0
Utilizing Class Separation Distance for the Evaluation of Corruption Robustness of Machine Learning Classifiers—0
Utilizing Generative Adversarial Networks for Image Data Augmentation and Classification of Semiconductor Wafer Dicing Induced Defects—0
VAE-based Feature Disentanglement for Data Augmentation and Compression in Generalized GNSS Interference Classification—0
VAE-Info-cGAN: Generating Synthetic Images by Combining Pixel-level and Feature-level Geospatial Conditional Inputs—0
Value-Based Reinforcement Learning for Continuous Control Robotic Manipulation in Multi-Task Sparse Reward Settings—0
Value-Consistent Representation Learning for Data-Efficient Reinforcement Learning—0
Variable frame rate-based data augmentation to handle speaking-style variability for automatic speaker verification—0
Variable-Speed Teaching-Playback as Real-World Data Augmentation for Imitation Learning—0
Variance Reduction in Deep Learning: More Momentum is All You Need—0
Goal-Conditioned Variational Autoencoder Trajectory Primitives with Continuous and Discrete Latent Codes—0
Variational Autoencoding of PDE Inverse Problems—0
Variational Mode Decomposition as Trusted Data Augmentation in ML-based Power System Stability Assessment—0
Vec2Node: Self-training with Tensor Augmentation for Text Classification with Few Labels—0
vec2text with Round-Trip Translations—0
Vector Quantization With Self-Attention for Quality-Independent Representation Learning—0
Vehicle Detection Performance in Nordic Region—0
VehicleGAN: Pair-flexible Pose Guided Image Synthesis for Vehicle Re-identification—0
VEnhancer: Generative Space-Time Enhancement for Video Generation—0
VIBR: Learning View-Invariant Value Functions for Robust Visual Control—0
Vicinal Counting Networks—0
Vicinal Feature Statistics Augmentation for Federated 3D Medical Volume Segmentation—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