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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 6976–7000 of 8378 papers

TitleStatusHype
ACGAN-based Data Augmentation Integrated with Long-term Scalogram for Acoustic Scene Classification—0
Generative Adversarial Networks for Bitcoin Data Augmentation—0
Learning Robust Feature Representations for Scene Text Detection—0
Towards a Robust WiFi-based Fall Detection with Adversarial Data Augmentation—0
A Joint Pixel and Feature Alignment Framework for Cross-dataset Palmprint RecognitionCode1
ON-TRAC Consortium for End-to-End and Simultaneous Speech Translation Challenge Tasks at IWSLT 2020—0
Networks with pixels embedding: a method to improve noise resistance in images classificationCode0
DeltaPy: A Framework for Tabular Data Augmentation in PythonCode1
Microphone Array Based Surveillance Audio Classification—0
Graph Random Neural Network for Semi-Supervised Learning on GraphsCode1
Multistream CNN for Robust Acoustic Modeling—0
Fluent Response Generation for Conversational Question AnsweringCode1
Training Keyword Spotting Models on Non-IID Data with Federated Learning—0
ASAPP-ASR: Multistream CNN and Self-Attentive SRU for SOTA Speech Recognition—0
Cross-Domain Few-Shot Learning with Meta Fine-Tuning—0
What Makes for Good Views for Contrastive Learning?—0
Lung Segmentation from Chest X-rays using Variational Data ImputationCode1
AutoML Segmentation for 3D Medical Image Data: Contribution to the MSD Challenge 2018Code1
Improving Deep Learning Models via Constraint-Based Domain Knowledge: a Brief Survey—0
Triple-GAIL: A Multi-Modal Imitation Learning Framework with Generative Adversarial Nets—0
Iterative Pseudo-Labeling for Speech RecognitionCode0
The NTNU System at the Interspeech 2020 Non-Native Children's Speech ASR Challenge—0
Feature Transformation Ensemble Model with Batch Spectral Regularization for Cross-Domain Few-Shot Classification—0
On the effectiveness of GAN generated cardiac MRIs for segmentation—0
Throwing Darts in the Dark? Detecting Bots with Limited Data using Neural 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 2×96d) (Faster AA)Percentage error2—Unverified
5Shake-Shake (26 2×112d) (Faster AA)Percentage error2—Unverified
#ModelMetricClaimedVerifiedStatus
1DiffAugClassification Accuracy92.7—Unverified
2PaCMAPClassification Accuracy85.3—Unverified
3hNNEClassification Accuracy77.4—Unverified
4TopoAEClassification Accuracy74.6—Unverified