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 7301–7350 of 8378 papers

TitleStatusHype
libmolgrid: GPU Accelerated Molecular Gridding for Deep Learning ApplicationsCode0
Inception Architecture and Residual Connections in Classification of Breast Cancer Histology Images—0
What You See is What You Get: Exploiting Visibility for 3D Object DetectionCode0
DR-GAN: Conditional Generative Adversarial Network for Fine-Grained Lesion Synthesis on Diabetic Retinopathy Images—0
Goal-Conditioned Variational Autoencoder Trajectory Primitives with Continuous and Discrete Latent Codes—0
Selective Synthetic Augmentation with Quality Assurance—0
Automatic Financial Feature Construction—0
VideoDG: Generalizing Temporal Relations in Videos to Novel DomainsCode0
Explaining Sequence-Level Knowledge Distillation as Data-Augmentation for Neural Machine Translation—0
Data Augmentation for Deep Learning-based Radio Modulation Classification—0
A limited-size ensemble of homogeneous CNN/LSTMs for high-performance word classification—0
Easy-to-Hard: Leveraging Simple Questions for Complex Question Generation—0
Effective Data Augmentation Approaches to End-to-End Task-Oriented Dialogue—0
Let's Get Dirty: GAN Based Data Augmentation for Camera Lens Soiling Detection in Autonomous Driving—0
An Exploration of Data Augmentation and Sampling Techniques for Domain-Agnostic Question Answering—0
A deep learning based tool for automatic brain extraction from functional magnetic resonance images in rodentsCode0
Just Ask:An Interactive Learning Framework for Vision and Language Navigation—0
Language Model Bootstrapping Using Neural Machine Translation For Conversational Speech Recognition—0
Scalable Deep Generative Relational Model with High-Order Node DependenceCode0
Breaking the Glass Ceiling for Embedding-Based Classifiers for Large Output Spaces—0
Mean Shift Rejection: Training Deep Neural Networks Without Minibatch Statistics or Normalization—0
A Multilayered Block Network Model to Forecast Large Dynamic Transportation Graphs: an Application to US Air Transport—0
E-Stitchup: Data Augmentation for Pre-Trained Embeddings—0
Augmentation Methods on Monophonic Audio for Instrument Classification in Polyphonic MusicCode0
Patch Reordering: a Novel Way to Achieve Rotation and Translation Invariance in Convolutional Neural Networks—0
Music Source Separation in the Waveform Domain—0
PanDA: Panoptic Data Augmentation—0
Data Augmentation Using Adversarial Training for Construction-Equipment Classification—0
DeepSmartFuzzer: Reward Guided Test Generation For Deep LearningCode0
Enhancing Out-Of-Domain Utterance Detection with Data Augmentation Based on Word Embeddings—0
Task-Oriented Dialog Systems that Consider Multiple Appropriate Responses under the Same ContextCode1
Unsupervised Neural Sensor Models for Synthetic LiDAR Data Augmentation—0
Visualizing Point Cloud Classifiers by Curvature SmoothingCode0
Improving N-gram Language Models with Pre-trained Deep Transformer—0
GANkyoku: a Generative Adversarial Network for Shakuhachi MusicCode0
Computational Ceramicology—0
Generating Diverse Translation by Manipulating Multi-Head Attention—0
Improving Conditioning in Context-Aware Sequence to Sequence Models—0
DermGAN: Synthetic Generation of Clinical Skin Images with Pathology—0
The Origins and Prevalence of Texture Bias in Convolutional Neural Networks—0
On Using SpecAugment for End-to-End Speech Translation—0
Action Recognition Using Volumetric Motion RepresentationsCode0
Constructing Multiple Tasks for Augmentation: Improving Neural Image Classification With K-means FeaturesCode0
Counterfactual Vision-and-Language Navigation via Adversarial Path Sampling—0
Signed Input Regularization—0
Faster AutoAugment: Learning Augmentation Strategies using BackpropagationCode0
A Smartphone-Based Skin Disease Classification Using MobileNet CNN—0
Robustness to Capitalization Errors in Named Entity Recognition—0
Improving Robustness of Task Oriented Dialog Systems—0
Learning from Data-Rich Problems: A Case Study on Genetic Variant Calling—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