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 1451–1500 of 8378 papers

TitleStatusHype
Domain Generalization for In-Orbit 6D Pose Estimation—0
P-TA: Using Proximal Policy Optimization to Enhance Tabular Data Augmentation via Large Language Models—0
Discriminative Hamiltonian Variational Autoencoder for Accurate Tumor Segmentation in Data-Scarce Regimes—0
Deep Learning methodology for the identification of wood species using high-resolution macroscopic imagesCode0
Robust Channel Learning for Large-Scale Radio Speaker VerificationCode0
First-Order Manifold Data Augmentation for Regression LearningCode0
A Survey of Large Language Models for Financial Applications: Progress, Prospects and Challenges—0
Benchmarking Children's ASR with Supervised and Self-supervised Speech Foundation ModelsCode0
The data augmentation algorithm—0
QDA-SQL: Questions Enhanced Dialogue Augmentation for Multi-Turn Text-to-SQLCode1
Discrete Latent Perspective Learning for Segmentation and Detection—0
PIG: Prompt Images Guidance for Night-Time Scene ParsingCode0
ROAR: Reinforcing Original to Augmented Data Ratio Dynamics for Wav2Vec2.0 Based ASR—0
A Unified Data Augmentation Framework for Low-Resource Multi-Domain Dialogue GenerationCode0
Training-free Camera Control for Video Generation—0
POWN: Prototypical Open-World Node ClassificationCode0
Inclusive ASR for Disfluent Speech: Cascaded Large-Scale Self-Supervised Learning with Targeted Fine-Tuning and Data Augmentation—0
T-JEPA: A Joint-Embedding Predictive Architecture for Trajectory Similarity Computation—0
Variational Mode Decomposition as Trusted Data Augmentation in ML-based Power System Stability Assessment—0
Can Synthetic Audio From Generative Foundation Models Assist Audio Recognition and Speech Modeling?Code0
SViTT-Ego: A Sparse Video-Text Transformer for Egocentric Video—0
Plan, Generate and Complicate: Improving Low-resource Dialogue State Tracking via Easy-to-Difficult Zero-shot Data Augmentation—0
Enhancing Psychotherapy Counseling: A Data Augmentation Pipeline Leveraging Large Language Models for Counseling ConversationsCode0
SimGen: Simulator-conditioned Driving Scene Generation—0
Navigating the Shadows: Unveiling Effective Disturbances for Modern AI Content DetectorsCode2
You Don't Need Domain-Specific Data Augmentations When Scaling Self-Supervised Learning—0
Enhancing Activity Recognition After Stroke: Generative Adversarial Networks for Kinematic Data Augmentation—0
Low-Complexity Acoustic Scene Classification Using Parallel Attention-Convolution NetworkCode0
DemosaicFormer: Coarse-to-Fine Demosaicing Network for HybridEVS CameraCode1
DiffPop: Plausibility-Guided Object Placement Diffusion for Image Composition—0
Dataset Enhancement with Instance-Level AugmentationsCode1
Data Augmentation by Fuzzing for Neural Test Generation—0
Test-Time Fairness and Robustness in Large Language Models—0
Rethinking the impact of noisy labels in graph classification: A utility and privacy perspective—0
SPIN: Spacecraft Imagery for NavigationCode1
MM-KWS: Multi-modal Prompts for Multilingual User-defined Keyword SpottingCode1
Comparing Data Augmentation Methods for End-to-End Task-Oriented Dialog Systems—0
Equivariant Neural Tangent Kernels—0
Improving Deep Learning-based Automatic Cranial Defect Reconstruction by Heavy Data Augmentation: From Image Registration to Latent Diffusion Models—0
Data Augmentation for Multivariate Time Series Classification: An Experimental Study—0
Data Augmentation in Earth Observation: A Diffusion Model Approach—0
Solution for CVPR 2024 UG2+ Challenge Track on All Weather Semantic Segmentation—0
Efficient Topology-aware Data Augmentation for High-Degree Graph Neural NetworksCode0
Decision Mamba: A Multi-Grained State Space Model with Self-Evolution Regularization for Offline RLCode0
Select-Mosaic: Data Augmentation Method for Dense Small Object ScenesCode0
3D MRI Synthesis with Slice-Based Latent Diffusion Models: Improving Tumor Segmentation Tasks in Data-Scarce RegimesCode1
Enhancing human action recognition with GAN-based data augmentationCode0
TabPFGen -- Tabular Data Generation with TabPFNCode1
Annotating FrameNet via Structure-Conditioned Language GenerationCode0
Evaluating the Effectiveness of Data Augmentation for Emotion Classification in Low-Resource Settings—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