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.

Further readings:

( Image credit: Albumentations )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 7726–7750 of 8378 papers

TitleStatusHype
Imperfect ImaGANation: Implications of GANs Exacerbating Biases on Facial Data Augmentation and Snapchat Selfie Lenses—0
Implanting Synthetic Lesions for Improving Liver Lesion Segmentation in CT Exams—0
Implicit Counterfactual Data Augmentation for Robust Learning—0
Implicit Design Choices and Their Impact on Emotion Recognition Model Development and Evaluation—0
Semantic Data Augmentation based Distance Metric Learning for Domain Generalization—0
Importance-Aware Data Augmentation for Document-Level Neural Machine Translation—0
GenCode: A Generic Data Augmentation Framework for Boosting Deep Learning-Based Code Understanding—0
Importance of Data Loading Pipeline in Training Deep Neural Networks—0
Importance Sampling via Score-based Generative Models—0
Impossible Triangle: What's Next for Pre-trained Language Models?—0
Improved baselines for vision-language pre-training—0
Improved Bayesian Logistic Supervised Topic Models with Data Augmentation—0
Improved Classification of White Blood Cells with the Generative Adversarial Network and Deep Convolutional Neural Network—0
Improved Consistency Training for Semi-Supervised Sequence-to-Sequence ASR via Speech Chain Reconstruction and Self-Transcribing—0
Improved Cotton Leaf Disease Classification Using Parameter-Efficient Deep Learning Framework—0
Improved Data Augmentation for Translation Suggestion—0
Improved English to Hindi Multimodal Neural Machine Translation—0
Enhancing Generative Networks for Chest Anomaly Localization through Automatic Registration-Based Unpaired-to-Pseudo-Paired Training Data Translation—0
Improved Image-based Pose Regressor Models for Underwater Environments—0
Improved Lexically Constrained Decoding for Translation and Monolingual Rewriting—0
Improved Meta-Learning Training for Speaker Verification—0
Improved POS tagging for spontaneous, clinical speech using data augmentation—0
Improved Prosodic Clustering for Multispeaker and Speaker-independent Phoneme-level Prosody Control—0
Improved Recurrent Neural Networks for Session-based Recommendations—0
Training Robust Spiking Neural Networks on Neuromorphic Data with Spatiotemporal Fragments—0
Show:102550
← PrevPage 310 of 336Next →
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