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 3751–3800 of 8378 papers

TitleStatusHype
CrowNER at Rocling 2022 Shared Task: NER using MacBERT and Adversarial Training—0
Why is Winoground Hard? Investigating Failures in Visuolinguistic CompositionalityCode1
Towards Better Out-of-Distribution Generalization of Neural Algorithmic Reasoning TasksCode1
Augmentation Invariant Manifold Learning—0
Self-supervised Character-to-Character Distillation for Text RecognitionCode1
SADT: Combining Sharpness-Aware Minimization with Self-Distillation for Improved Model GeneralizationCode0
Exploring Train and Test-Time Augmentations for Audio-Language Learning—0
1Cademy @ Causal News Corpus 2022: Enhance Causal Span Detection via Beam-Search-based Position SelectorCode0
SAGE: Saliency-Guided Mixup with Optimal Rearrangements—0
Embedding Space Augmentation for Weakly Supervised Learning in Whole-Slide Images—0
Counterfactual Data Augmentation via Perspective Transition for Open-Domain DialoguesCode0
Speaker Representation Learning via Contrastive Loss with Maximal Speaker SeparabilityCode1
Differentiable Data Augmentation for Contrastive Sentence Representation LearningCode1
Interpretable CNN-Multilevel Attention Transformer for Rapid Recognition of Pneumonia from Chest X-Ray Images—0
Diverse Parallel Data Synthesis for Cross-Database Adaptation of Text-to-SQL Parsers—0
A Comparative Study of Graph Neural Networks for Shape Classification in NeuroimagingCode0
Anomaly Detection in Additive Manufacturing Processes using Supervised Classification with Imbalanced Sensor Data based on Generative Adversarial Network—0
RoChBert: Towards Robust BERT Fine-tuning for ChineseCode1
Random Utterance Concatenation Based Data Augmentation for Improving Short-video Speech Recognition—0
FUSSL: Fuzzy Uncertain Self Supervised Learning—0
Latent Space is Feature Space: Regularization Term for GANs Training on Limited DatasetCode0
Rawgment: Noise-Accounted RAW Augmentation Enables Recognition in a Wide Variety of Environments—0
U-Net-based Models for Skin Lesion Segmentation: More Attention and AugmentationCode1
GraphMAD: Graph Mixup for Data Augmentation using Data-Driven Convex ClusteringCode0
A Generic Shared Attention Mechanism for Various Backbone Neural Networks—0
An Adversarial Active Sampling-based Data Augmentation Framework for Manufacturable Chip Design—0
ScoreMix: A Scalable Augmentation Strategy for Training GANs with Limited Data—0
A knowledge-driven vowel-based approach of depression classification from speech using data augmentationCode0
FreeVC: Towards High-Quality Text-Free One-Shot Voice ConversionCode2
Make More of Your Data: Minimal Effort Data Augmentation for Automatic Speech Recognition and Translation—0
Towards customizable reinforcement learning agents: Enabling preference specification through online vocabulary expansion—0
Bridging the visual gap in VLN via semantically richer instructions—0
Domain Adaptive Object Detection for Autonomous Driving under Foggy WeatherCode1
Dictionary-Assisted Supervised Contrastive LearningCode0
Addressing Heterogeneity in Federated Learning via Distributional TransformationCode0
TuneUp: A Simple Improved Training Strategy for Graph Neural Networks—0
In search of strong embedding extractors for speaker diarisation—0
Long-tailed Food Classification—0
Pretrained audio neural networks for Speech emotion recognition in PortugueseCode0
Automatic Diagnosis of Myocarditis Disease in Cardiac MRI Modality using Deep Transformers and Explainable Artificial Intelligence—0
Weakly Supervised Data Augmentation Through Prompting for Dialogue Understanding—0
Learning to Augment via Implicit Differentiation for Domain Generalization—0
The Curious Case of Benign Memorization—0
On Robust Incremental Learning over Many Multilingual Steps—0
I Prefer not to Say: Protecting User Consent in Models with Optional Personal DataCode0
'A net for everyone': fully personalized and unsupervised neural networks trained with longitudinal data from a single patient—0
Multi-Domain Long-Tailed Learning by Augmenting Disentangled RepresentationsCode0
Efficiently Trained Low-Resource Mongolian Text-to-Speech System Based On FullConv-TTS—0
Provably Learning Diverse Features in Multi-View Data with Midpoint MixupCode0
Sufficient Invariant Learning for Distribution Shift—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