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 2851–2900 of 8378 papers

TitleStatusHype
Pose-dIVE: Pose-Diversified Augmentation with Diffusion Model for Person Re-Identification—0
Multimodal Physiological Signals Representation Learning via Multiscale Contrasting for Depression Recognition—0
PathoWAve: A Deep Learning-based Weight Averaging Method for Improving Domain Generalization in Histopathology ImagesCode0
From Overfitting to Robustness: Quantity, Quality, and Variety Oriented Negative Sample Selection in Graph Contrastive Learning—0
Self-supervised Brain Lesion Generation for Effective Data Augmentation of Medical Images—0
Exploring Audio-Visual Information Fusion for Sound Event Localization and Detection In Low-Resource Realistic Scenarios—0
FIESTA: Fourier-Based Semantic Augmentation with Uncertainty Guidance for Enhanced Domain Generalizability in Medical Image Segmentation—0
Urban-Focused Multi-Task Offline Reinforcement Learning with Contrastive Data Sharing—0
Factual Dialogue Summarization via Learning from Large Language Models—0
Self-Supervised Pretext Tasks for Alzheimer's Disease Classification using 3D Convolutional Neural Networks on Large-Scale Synthetic Neuroimaging Dataset—0
Improving Zero-Shot Cross-Lingual Transfer via Progressive Code-Switching—0
A New Approach for Evaluating and Improving the Performance of Segmentation Algorithms on Hard-to-Detect Blood VesselsCode0
Fighting Randomness with Randomness: Mitigating Optimisation Instability of Fine-Tuning using Delayed Ensemble and Noisy InterpolationCode0
Insect Identification in the Wild: The AMI DatasetCode0
Agriculture-Vision Challenge 2024 -- The Runner-Up Solution for Agricultural Pattern Recognition via Class Balancing and Model Ensemble—0
Composited-Nested-Learning with Data Augmentation for Nested Named Entity Recognition—0
Skin Cancer Images Classification using Transfer Learning Techniques—0
Self-Supervised Time-Series Anomaly Detection Using Learnable Data Augmentation—0
MMUTF: Multimodal Multimedia Event Argument Extraction with Unified Template Filling—0
Class-specific Data Augmentation for Plant Stress ClassificationCode0
Depth Anywhere: Enhancing 360 Monocular Depth Estimation via Perspective Distillation and Unlabeled Data Augmentation—0
Beyond Visual Appearances: Privacy-sensitive Objects Identification via Hybrid Graph Reasoning—0
Is Your HD Map Constructor Reliable under Sensor Corruptions?—0
Visually Robust Adversarial Imitation Learning from Videos with Contrastive LearningCode0
P-TA: Using Proximal Policy Optimization to Enhance Tabular Data Augmentation via Large Language Models—0
Enhancing and Assessing Instruction-Following with Fine-Grained Instruction Variants—0
Multispectral Snapshot Image Registration Using Learned Cross Spectral Disparity Estimation and a Deep Guided Occlusion Reconstruction NetworkCode0
Domain Generalization for In-Orbit 6D Pose Estimation—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
First-Order Manifold Data Augmentation for Regression LearningCode0
Robust Channel Learning for Large-Scale Radio Speaker VerificationCode0
A Survey of Large Language Models for Financial Applications: Progress, Prospects and Challenges—0
PIG: Prompt Images Guidance for Night-Time Scene ParsingCode0
The data augmentation algorithm—0
Discrete Latent Perspective Learning for Segmentation and Detection—0
Benchmarking Children's ASR with Supervised and Self-supervised Speech Foundation ModelsCode0
A Unified Data Augmentation Framework for Low-Resource Multi-Domain Dialogue GenerationCode0
Inclusive ASR for Disfluent Speech: Cascaded Large-Scale Self-Supervised Learning with Targeted Fine-Tuning and Data Augmentation—0
ROAR: Reinforcing Original to Augmented Data Ratio Dynamics for Wav2Vec2.0 Based ASR—0
Training-free Camera Control for Video Generation—0
POWN: Prototypical Open-World Node ClassificationCode0
Enhancing Psychotherapy Counseling: A Data Augmentation Pipeline Leveraging Large Language Models for Counseling ConversationsCode0
T-JEPA: A Joint-Embedding Predictive Architecture for Trajectory Similarity Computation—0
Can Synthetic Audio From Generative Foundation Models Assist Audio Recognition and Speech Modeling?Code0
You Don't Need Domain-Specific Data Augmentations When Scaling Self-Supervised Learning—0
Plan, Generate and Complicate: Improving Low-resource Dialogue State Tracking via Easy-to-Difficult Zero-shot Data Augmentation—0
SimGen: Simulator-conditioned Driving Scene Generation—0
SViTT-Ego: A Sparse Video-Text Transformer for Egocentric Video—0
Variational Mode Decomposition as Trusted Data Augmentation in ML-based Power System Stability Assessment—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