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 4851–4900 of 8378 papers

TitleStatusHype
BioInfo@UAVR@SMM4H’22: Classification and Extraction of Adverse Event mentions in Tweets using Transformer Models—0
Position Offset Label Prediction for Grammatical Error Correction—0
MALM: Mixing Augmented Language Modeling for Zero-Shot Machine Translation—0
Document-level Event Factuality Identification via Machine Reading Comprehension Frameworks with Transfer Learning—0
Rethinking Data Augmentation in Text-to-text Paradigm—0
Data Augmentation for Improving the Prediction of Validity and Novelty of Argumentative Conclusions—0
Dynamic Nonlinear Mixup with Distance-based Sample Selection—0
Table-based Fact Verification with Self-labeled Keypoint Alignment—0
Summarizing Patients’ Problems from Hospital Progress Notes Using Pre-trained Sequence-to-Sequence Models—0
Coordination Generation via Synchronized Text-Infilling—0
Augmented Bio-SBERT: Improving Performance for Pairwise Sentence Tasks in Bio-medical Domain—0
The Only Chance to Understand: Machine Translation of the Severely Endangered Low-resource Languages of Eurasia—0
Addressing Limitations of Encoder-Decoder Based Approach to Text-to-SQL—0
Probing the Robustness of Pre-trained Language Models for Entity MatchingCode0
Improving Event Temporal Relation Classification via Auxiliary Label-Aware Contrastive Learning—0
Evaluating and Mitigating Inherent Linguistic Bias of African American English through Inference—0
KUL@SMM4H’22: Template Augmented Adaptive Pre-training for Tweet Classification—0
CAISA@SMM4H’22: Robust Cross-Lingual Detection of Disease Mentions on Social Media with Adversarial Methods—0
ParaZh-22M: A Large-Scale Chinese Parabank via Machine Translation—0
Towards Robust Neural Retrieval with Source Domain Synthetic Pre-Finetuning—0
BRCC and SentiBahasaRojak: The First Bahasa Rojak Corpus for Pretraining and Sentiment Analysis Dataset—0
Towards Summarizing Healthcare Questions in Low-Resource Setting—0
Effective Data Augmentation for Sentence Classification Using One VAE per Class—0
Pseudo-Label Generation and Various Data Augmentation for Semi-Supervised Hyperspectral Object DetectionCode0
Domain Generalization -- A Causal Perspective—0
S2P: State-conditioned Image Synthesis for Data Augmentation in Offline Reinforcement LearningCode0
Where Should I Spend My FLOPS? Efficiency Evaluations of Visual Pre-training Methods—0
Using Knowledge Distillation to improve interpretable models in a retail banking context—0
Augmentation BackdoorsCode0
Prompt-guided Scene Generation for 3D Zero-Shot Learning—0
Automatic Data Augmentation via Invariance-Constrained LearningCode0
Named Entity Recognition in Industrial Tables using Tabular Language Models—0
Contrastive Unsupervised Learning of World Model with Invariant Causal Features—0
Weighted Contrastive HashingCode0
Synthesizing Annotated Image and Video Data Using a Rendering-Based Pipeline for Improved License Plate Recognition—0
Data Augmentation using Feature Generation for Volumetric Medical Images—0
3D Rendering Framework for Data Augmentation in Optical Character Recognition—0
TaskMix: Data Augmentation for Meta-Learning of Spoken Intent Understanding—0
Ani-GIFs: A benchmark dataset for domain generalization of action recognition from GIFs—0
On the Impact of Speech Recognition Errors in Passage Retrieval for Spoken Question AnsweringCode0
Contrastive learning for unsupervised medical image clustering and reconstruction—0
A Simple Strategy to Provable Invariance via Orbit Mapping—0
Towards Bridging the Space Domain Gap for Satellite Pose Estimation using Event Sensing—0
Semantically Consistent Data Augmentation for Neural Machine Translation via Conditional Masked Language ModelCode0
Automated detection of Alzheimer disease using MRI images and deep neural networks- A review—0
StyleTime: Style Transfer for Synthetic Time Series Generation—0
Scope of Pre-trained Language Models for Detecting Conflicting Health Information—0
SR-GCL: Session-Based Recommendation with Global Context Enhanced Augmentation in Contrastive Learning—0
DARTSRepair: Core-failure-set Guided DARTS for Network Robustness to Common Corruptions—0
Exploring Inconsistent Knowledge Distillation for Object Detection with Data AugmentationCode0
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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