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 6401–6450 of 8378 papers

TitleStatusHype
Semi-supervised Interactive Intent Labeling—0
Machine Learning Algorithms for Breast Cancer Detection in Mammography Images: A Comparative Study—0
Semantic Data Augmentation for End-to-End Mandarin Speech Recognition—0
Accuracy Improvement for Fully Convolutional Networks via Selective Augmentation with Applications to Electrocardiogram Data—0
Automatic Diagnosis of COVID-19 from CT Images using CycleGAN and Transfer Learning—0
DeepMix: Online Auto Data Augmentation for Robust Visual Object Tracking—0
Disfluency Detection with Unlabeled Data and Small BERT Models—0
Exploring 2D Data Augmentation for 3D Monocular Object Detection—0
TITAN: T Cell Receptor Specificity Prediction with Bimodal Attention Networks—0
PALI at SemEval-2021 Task 2: Fine-Tune XLM-RoBERTa for Word in Context Disambiguation—0
GAN-Based Data Augmentation and Anonymization for Skin-Lesion Analysis: A Critical ReviewCode0
Boosting Masked Face Recognition with Multi-Task ArcFace—0
Does enhanced shape bias improve neural network robustness to common corruptions?—0
Automatic Stroke Classification of Tabla Accompaniment in Hindustani Vocal Concert Audio—0
Estimating Traffic Speeds using Probe Data: A Deep Neural Network Approach—0
A Competitive Method to VIPriors Object Detection Challenge—0
Machine-learning-based head impact subtyping based on the spectral densities of the measurable head kinematics—0
Few-shot learning via tensor hallucinationCode0
On Training Sketch Recognizers for New Domains—0
Combining a Convolutional Neural Network with Autoencoders to Predict the Survival Chance of COVID-19 Patients—0
Best Practices for Noise-Based Augmentation to Improve the Performance of Deployable Speech-Based Emotion Recognition Systems—0
Color Variants Identification in Fashion e-commerce via Contrastive Self-Supervised Representation Learning—0
Sentence Concatenation Approach to Data Augmentation for Neural Machine Translation—0
Age Range Estimation using MTCNN and VGG-Face Model—0
Hierarchical Topic Presence Models—0
Open data for Moroccan license plates for OCR applications : data collection, labeling, and model construction—0
Data Augmentation for Voice-Assistant NLU using BERT-based Interchangeable Rephrase—0
EnvGAN: Adversarial Synthesis of Environmental Sounds for Data Augmentation—0
On the Robustness of Intent Classification and Slot Labeling in Goal-oriented Dialog Systems to Real-world NoiseCode0
Multilingual Transfer Learning for Code-Switched Language and Speech Neural Modeling—0
Can a Transformer Pass the Wug Test? Tuning Copying Bias in Neural Morphological Inflection Models—0
Generalization bounds via distillation—0
Exploring Geometric Consistency for Monocular 3D Object Detection—0
Noether: The More Things Change, the More Stay the Same—0
Constructing Contrastive samples via Summarization for Text Classification with limited annotationsCode0
ALT-MAS: A Data-Efficient Framework for Active Testing of Machine Learning Algorithms—0
The NTNU Taiwanese ASR System for Formosa Speech Recognition Challenge 2020—0
RaidaR: A Rich Annotated Image Dataset of Rainy Street Scenes—0
Robust Training of Social Media Image Classification Models for Rapid Disaster Response—0
Exploring Machine Speech Chain for Domain Adaptation and Few-Shot Speaker Adaptation—0
Lone Pine at SemEval-2021 Task 5: Fine-Grained Detection of Hate Speech Using BERToxicCode0
Handling Climate Change Using Counterfactuals: Using Counterfactuals in Data Augmentation to Predict Crop Growth in an Uncertain Climate Future—0
Analysis Towards Classification of Infection and Ischaemia of Diabetic Foot Ulcers—0
Correlation Sketches for Approximate Join-Correlation Queries—0
Bootstrapping Your Own Positive Sample: Contrastive Learning With Electronic Health Record Data—0
Track, Check, Repeat: An EM Approach to Unsupervised Tracking—0
Generalization of GANs and overparameterized models under Lipschitz continuity—0
Achieving Domain Generalization in Underwater Object Detection by Domain Mixup and Contrastive Learning—0
Personalized Speech Enhancement through Self-Supervised Data Augmentation and Purification—0
On-the-Fly Aligned Data Augmentation for Sequence-to-Sequence ASRCode0
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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