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 4951–5000 of 8378 papers

TitleStatusHype
Semantic Certainty Assessment in Vector Retrieval Systems: A Novel Framework for Embedding Quality Evaluation—0
Semantic Data Augmentation for End-to-End Mandarin Speech Recognition—0
Semantic Data Augmentation for Long-tailed Facial Expression Recognition—0
Semantic Embedding Space for Zero-Shot Action Recognition—0
Semantic Equivariant Mixup—0
Semantic Image Synthesis for Abdominal CT—0
On-Air Deep Learning Integrated Semantic Inference Models for Enhanced Earth Observation Satellite Networks—0
Semantic Positive Pairs for Enhancing Visual Representation Learning of Instance Discrimination methods—0
Semantics-Depth-Symbiosis: Deeply Coupled Semi-Supervised Learning of Semantics and Depth—0
Semantics-Preserved Data Augmentation for Aspect-Based Sentiment Analysis—0
Semantic Style Transfer for Enhancing Animal Facial Landmark Detection—0
SemAug: Semantically Meaningful Image Augmentations for Object Detection Through Language Grounding—0
SemEval-2022 Task 3: PreTENS-Evaluating Neural Networks on Presuppositional Semantic Knowledge—0
SemI2I: Semantically Consistent Image-to-Image Translation for Domain Adaptation of Remote Sensing Data—0
SemiFed: Semi-supervised Federated Learning with Consistency and Pseudo-Labeling—0
SemiHand: Semi-Supervised Hand Pose Estimation With Consistency—0
Semi-self-supervised Automated ICD Coding—0
Semi-supervised 2D Human Pose Estimation via Adaptive Keypoint Masking—0
Semi-supervised ASR by End-to-end Self-training—0
Semi-supervised binary classification with latent distance learning—0
GSDA: Generative Adversarial Network-based Semi-Supervised Data Augmentation for Ultrasound Image Classification—0
Semi-Supervised Deep Metrics for Image Registration—0
Semi-supervised Domain Adaptation for Semantic Segmentation—0
Semi-supervised Feature Learning For Improving Writer Identification—0
Semi-supervised Federated Learning for Activity Recognition—0
Semi-Supervised Few-Shot Intent Classification and Slot Filling—0
Semi-Supervised Heterogeneous Graph Learning with Multi-level Data Augmentation—0
Semi-Supervised Image Captioning Considering Wasserstein Graph Matching—0
Semi-supervised Interactive Intent Labeling—0
Semi-Supervised Learning and Data Augmentation in Wearable-based Momentary Stress Detection in the Wild—0
Semi-supervised learning by selective training with pseudo labels via confidence estimation—0
Semi-Supervised Learning for Multi-Label Cardiovascular Diseases Prediction:A Multi-Dataset Study—0
Semi-supervised learning method based on predefined evenly-distributed class centroids—0
Semi-Supervised Learning of Semantic Correspondence with Pseudo-Labels—0
Semi-Supervised Learning via Swapped Prediction for Communication Signal Recognition—0
Semi-Supervised Learning with Data Augmentation for End-to-End ASR—0
Semi-Supervised Learning with Mutual Distillation for Monocular Depth Estimation—0
Semi-supervised Object Detection: A Survey on Recent Research and Progress—0
Semi-Supervised Object Detection: A Survey on Progress from CNN to Transformer—0
Semi-supervised Relation Extraction via Data Augmentation and Consistency-training—0
Semi-Supervised RF Fingerprinting with Consistency-Based Regularization—0
Semi-supervised Semantic Segmentation of Prostate and Organs-at-Risk on 3D Pelvic CT Images—0
Semi-Supervised Singing Voice Separation with Noisy Self-Training—0
Semi-supervised transfer learning for language expansion of end-to-end speech recognition models to low-resource languages—0
Semi-Supervising Learning, Transfer Learning, and Knowledge Distillation with SimCLR—0
Sensitivity and Generalization in Neural Networks: an Empirical Study—0
Sensitivity-Informed Augmentation for Robust Segmentation—0
Sensor Data Augmentation by Resampling for Contrastive Learning in Human Activity Recognition—0
Sentence Boundary Augmentation For Neural Machine Translation Robustness—0
Sentence Concatenation Approach to Data Augmentation for Neural Machine Translation—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