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 3701–3750 of 8378 papers

TitleStatusHype
Empirical Evaluation of the Segment Anything Model (SAM) for Brain Tumor Segmentation—0
Cross-Task Data Augmentation by Pseudo-label Generation for Region Based Coronary Artery Instance Segmentation—0
Parameterizing Context: Unleashing the Power of Parameter-Efficient Fine-Tuning and In-Context Tuning for Continual Table Semantic Parsing—0
Offline Imitation Learning with Variational Counterfactual ReasoningCode0
Whole Slide Multiple Instance Learning for Predicting Axillary Lymph Node MetastasisCode0
T-GAE: Transferable Graph Autoencoder for Network AlignmentCode0
How Good Are Synthetic Medical Images? An Empirical Study with Lung UltrasoundCode0
Accelerated Neural Network Training with Rooted Logistic Objectives—0
Beyond Random Augmentations: Pretraining with Hard ViewsCode0
Untargeted White-box Adversarial Attack with Heuristic Defence Methods in Real-time Deep Learning based Network Intrusion Detection System—0
CLEVRER-Humans: Describing Physical and Causal Events the Human Way—0
BTDNet: a Multi-Modal Approach for Brain Tumor Radiogenomic Classification—0
Latent Filling: Latent Space Data Augmentation for Zero-shot Speech Synthesis—0
A Survey of GPT-3 Family Large Language Models Including ChatGPT and GPT-4—0
Boosting Dermatoscopic Lesion Segmentation via Diffusion Models with Visual and Textual Prompts—0
Hate Speech Detection in Limited Data Contexts using Synthetic Data Generation—0
MedDiffusion: Boosting Health Risk Prediction via Diffusion-based Data Augmentation—0
Multi-Resolution Fusion for Fully Automatic Cephalometric Landmark Detection—0
Randomized Dimension Reduction with Statistical Guarantees—0
What do we learn from a large-scale study of pre-trained visual representations in sim and real environments?—0
Learnable Data Augmentation for One-Shot Unsupervised Domain AdaptationCode0
Understanding Masked Autoencoders From a Local Contrastive Perspective—0
Fetal-BET: Brain Extraction Tool for Fetal MRICode0
Target-Aware Contextual Political Bias Detection in News—0
Synthetic Data Generation in Low-Resource Settings via Fine-Tuning of Large Language Models—0
Incorporating Supervised Domain Generalization into Data Augmentation—0
It's all about you: Personalized in-Vehicle Gesture Recognition with a Time-of-Flight Camera—0
TDCGL: Two-Level Debiased Contrastive Graph Learning for Recommendation—0
Robust Sentiment Analysis for Low Resource languages Using Data Augmentation Approaches: A Case Study in Marathi—0
Understanding Robust Overfitting from the Feature Generalization Perspective—0
A Unified Framework for Generative Data Augmentation: A Comprehensive Survey—0
Pixel-Inconsistency Modeling for Image Manipulation Localization—0
Structural Adversarial Objectives for Self-Supervised Representation LearningCode0
Anomaly Detection in Power Generation Plants with Generative Adversarial Networks—0
Unsupervised Large Language Model Alignment for Information Retrieval via Contrastive Feedback—0
Asynchronous Graph GeneratorCode0
On the Equivalence of Graph Convolution and MixupCode0
Investigating Shift Equivalence of Convolutional Neural Networks in Industrial Defect SegmentationCode0
SatDM: Synthesizing Realistic Satellite Image with Semantic Layout Conditioning using Diffusion ModelsCode0
Augment to Interpret: Unsupervised and Inherently Interpretable Graph EmbeddingsCode0
UPB @ ACTI: Detecting Conspiracies using fine tuned Sentence Transformers—0
Feature Normalization Prevents Collapse of Non-contrastive Learning Dynamics—0
Beyond Gut Feel: Using Time Series Transformers to Find Investment Gems—0
Learning to Transform for Generalizable Instance-wise InvarianceCode0
Using Weak Supervision and Data Augmentation in Question Answering—0
Label Augmentation Method for Medical Landmark Detection in Hip Radiograph ImagesCode0
Exploring Self-Supervised Contrastive Learning of Spatial Sound Event Representation—0
Targeted Image Data Augmentation Increases Basic Skills Captioning Robustness—0
Defending Against Physical Adversarial Patch Attacks on Infrared Human Detection—0
Generalization of pixel-wise phase estimation by CNN and improvement of phase-unwrapping by MRF optimization for one-shot 3D scan—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