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 7151–7200 of 8378 papers

TitleStatusHype
Synthesizing lesions using contextual GANs improves breast cancer classification on mammograms—0
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
Networks with pixels embedding: a method to improve noise resistance in images classificationCode0
ON-TRAC Consortium for End-to-End and Simultaneous Speech Translation Challenge Tasks at IWSLT 2020—0
Microphone Array Based Surveillance Audio Classification—0
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
Multistream CNN for Robust Acoustic Modeling—0
Cross-Domain Few-Shot Learning with Meta Fine-Tuning—0
What Makes for Good Views for Contrastive Learning?—0
Triple-GAIL: A Multi-Modal Imitation Learning Framework with Generative Adversarial Nets—0
Improving Deep Learning Models via Constraint-Based Domain Knowledge: a Brief Survey—0
Iterative Pseudo-Labeling for Speech RecognitionCode0
On the effectiveness of GAN generated cardiac MRIs for segmentation—0
Feature Transformation Ensemble Model with Batch Spectral Regularization for Cross-Domain Few-Shot Classification—0
The NTNU System at the Interspeech 2020 Non-Native Children's Speech ASR Challenge—0
Throwing Darts in the Dark? Detecting Bots with Limited Data using Neural Data Augmentation—0
FuCiTNet: Improving the generalization of deep learning networks by the fusion of learned class-inherent transformationsCode0
Leveraging Affective Bidirectional Transformers for Offensive Language Detection—0
Target Speech Extraction Based on Blind Source Separation and X-vector-based Speaker Selection Trained with Data AugmentationCode0
"I have vxxx bxx connexxxn!": Facing Packet Loss in Deep Speech Emotion Recognition—0
KEIS@JUST at SemEval-2020 Task 12: Identifying Multilingual Offensive Tweets Using Weighted Ensemble and Fine-Tuned BERT—0
VirAAL: Virtual Adversarial Active Learning For NLUCode0
You Do Not Need More Data: Improving End-To-End Speech Recognition by Text-To-Speech Data Augmentation—0
Proxy Experience Replay: Federated Distillation for Distributed Reinforcement Learning—0
ODVICE: An Ontology-Driven Visual Analytic Tool for Interactive Cohort Extraction—0
Towards Robustifying NLI Models Against Lexical Dataset BiasesCode0
Deep Learning on Point Clouds for False Positive Reduction at Nodule Detection in Chest CT Scans—0
Data Augmentation via Mixed Class Interpolation using Cycle-Consistent Generative Adversarial Networks Applied to Cross-Domain Imagery—0
Data Augmentation for Hypernymy DetectionCode0
Correcting the Autocorrect: Context-Aware Typographical Error Correction via Training Data Augmentation—0
Self-Training with Improved Regularization for Sample-Efficient Chest X-Ray Classification—0
A Causal View on Robustness of Neural Networks—0
A Comprehensive Survey of Grammar Error Correction—0
Improving Non-autoregressive Neural Machine Translation with Monolingual Data—0
Neural Symbolic Reader: Scalable Integration of Distributed and Symbolic Representations for Reading Comprehension—0
Augmenting Small Data to Classify Contextualized Dialogue Acts for Exploratory Visualization—0
When is Multi-task Learning Beneficial for Low-Resource Noisy Code-switched User-generated Algerian Texts?—0
Stigma Annotation Scheme and Stigmatized Language Detection in Health-Care Discussions on Social Media—0
On the Benefits of Invariance in Neural Networks—0
Multilingual Neural Machine Translation involving Indian Languages—0
Data Augmentation using Machine Translation for Fake News Detection in the Urdu Language—0
Getting More Data for Low-resource Morphological Inflection: Language Models and Data Augmentation—0
Multiword Expression aware Neural Machine Translation—0
Conditional Augmentation for Aspect Term Extraction via Masked Sequence-to-Sequence Generation—0
Does Data Augmentation Improve Generalization in NLP?—0
Representations of Syntax [MASK] Useful: Effects of Constituency and Dependency Structure in Recursive LSTMsCode0
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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