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 6351–6400 of 8378 papers

TitleStatusHype
LIIR at SemEval-2021 task 6: Detection of Persuasion Techniques In Texts and Images using CLIP features—0
HiddenCut: Simple Data Augmentation for Natural Language Understanding with Better GeneralizationCode0
Cascaded Diffusion Models for High Fidelity Image Generation—0
EDDA: Explanation-driven Data Augmentation to Improve Explanation Faithfulness—0
Data Augmentation for Text Generation Without Any Augmented Data—0
Audio-visual scene classification: analysis of DCASE 2021 Challenge submissions—0
Not Far Away, Not So Close: Sample Efficient Nearest Neighbour Data Augmentation via MiniMaxCode0
Towards More Equitable Question Answering Systems: How Much More Data Do You Need?Code0
Drawing Multiple Augmentation Samples Per Image During Training Efficiently Decreases Test Error—0
The Imaginative Generative Adversarial Network: Automatic Data Augmentation for Dynamic Skeleton-Based Hand Gesture and Human Action Recognition—0
Contrastive Fine-tuning Improves Robustness for Neural Rankers—0
Adversarial Attack Driven Data Augmentation for Accurate And Robust Medical Image Segmentation—0
GraphVICRegHSIC: Towards improved self-supervised representation learning for graphs with a hyrbid loss functionCode0
Enhance Multimodal Model Performance with Data Augmentation: Facebook Hateful Meme Challenge SolutionCode0
Data Expansion using Back Translation and Paraphrasing for Hate Speech Detection—0
Properties of the After Kernel—0
Combining Transformer Generators with Convolutional Discriminators—0
Prostate Gland Segmentation in Histology Images via Residual and Multi-Resolution U-Net—0
DPN-SENet:A self-attention mechanism neural network for detection and diagnosis of COVID-19 from chest x-ray imagesCode0
Nonlinear Hawkes Process with Gaussian Process Self Effects—0
A Lightweight Privacy-Preserving Scheme Using Label-based Pixel Block Mixing for Image Classification in Deep LearningCode0
Speech & Song Emotion Recognition Using Multilayer Perceptron and Standard Vector Machine—0
Self-Learning for Received Signal Strength Map Reconstruction with Neural Architecture Search—0
A Fusion-Denoising Attack on InstaHide with Data AugmentationCode0
Data Augmentation for Sign Language Gloss Translation—0
Global Wheat Challenge 2020: Analysis of the competition design and winning models—0
Bootstrapping User and Item Representations for One-Class Collaborative Filtering—0
Object-Based Augmentation Improves Quality of Remote Sensing Semantic Segmentation—0
Label Geometry Aware Discriminator for Conditional Generative Networks—0
Cross-Modal Generative Augmentation for Visual Question Answering—0
Robust Training Using Natural Transformation—0
Examining and Mitigating Kernel Saturation in Convolutional Neural Networks using Negative Images—0
The Tags Are Alright: Robust Large-Scale RFID Clone Detection Through Federated Data-Augmented Radio Fingerprinting—0
De-Pois: An Attack-Agnostic Defense against Data Poisoning Attacks—0
Topological Uncertainty: Monitoring trained neural networks through persistence of activation graphs—0
Rethinking Ultrasound Augmentation: A Physics-Inspired Approach—0
Safety Enhancement for Deep Reinforcement Learning in Autonomous Separation Assurance—0
R2U3D: Recurrent Residual 3D U-Net for Lung Segmentation—0
PingAn-VCGroup's Solution for ICDAR 2021 Competition on Scientific Table Image Recognition to Latex—0
Representation Learning for Clustering via Building ConsensusCode0
Data Augmentation by Concatenation for Low-Resource Translation: A Mystery and a Solution—0
Consistency and Monotonicity Regularization for Neural Knowledge Tracing—0
AirMixML: Over-the-Air Data Mixup for Inherently Privacy-Preserving Edge Machine Learning—0
Unsupervised data augmentation for object detection—0
Adapting Coreference Resolution for Processing Violent Death Narratives—0
TabAug: Data Driven Augmentation for Enhanced Table Structure RecognitionCode0
Twin Systems for DeepCBR: A Menagerie of Deep Learning and Case-Based Reasoning Pairings for Explanation and Data Augmentation—0
Autoregressive Dynamics Models for Offline Policy Evaluation and Optimization—0
Randomized Histogram Matching: A Simple Augmentation for Unsupervised Domain Adaptation in Overhead Imagery—0
Towards Fair Federated Learning with Zero-Shot Data Augmentation—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