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 5401–5450 of 8378 papers

TitleStatusHype
The ADAPT Centre’s Neural MT Systems for the WAT 2020 Document-Level Translation Task—0
The AI Mechanic: Acoustic Vehicle Characterization Neural Networks—0
The ASRU 2019 Mandarin-English Code-Switching Speech Recognition Challenge: Open Datasets, Tracks, Methods and Results—0
The Benefits of Mixup for Feature Learning—0
The CUHK-TENCENT speaker diarization system for the ICASSP 2022 multi-channel multi-party meeting transcription challenge—0
The Curious Case of Benign Memorization—0
The data augmentation algorithm—0
The DKU Replay Detection System for the ASVspoof 2019 Challenge: On Data Augmentation, Feature Representation, Classification, and Fusion—0
The Dual-use Dilemma in LLMs: Do Empowering Ethical Capacities Make a Degraded Utility?—0
The Effectiveness of Data Augmentation for Detection of Gastrointestinal Diseases from Endoscopical Images—0
The Effect of Data Augmentation on Classification of Atrial Fibrillation in Short Single-Lead ECG Signals Using Deep Neural Networks—0
The effects of gender bias in word embeddings on depression prediction—0
The Effects of Hallucinations in Synthetic Training Data for Relation Extraction—0
The Effects of Regularization and Data Augmentation are Class Dependent—0
The Efficacy of Semantics-Preserving Transformations in Self-Supervised Learning for Medical Ultrasound—0
The FBK Participation in the WMT 2016 Automatic Post-editing Shared Task—0
The FruitShell French synthesis system at the Blizzard 2023 Challenge—0
The Geometry of Self-supervised Learning Models and its Impact on Transfer Learning—0
The Hidden Influence of Latent Feature Magnitude When Learning with Imbalanced Data—0
The identification of garbage dumps in the rural areas of Cyprus through the application of deep learning to satellite imagery—0
The Illusion of Role Separation: Hidden Shortcuts in LLM Role Learning (and How to Fix Them)—0
The Imaginative Generative Adversarial Network: Automatic Data Augmentation for Dynamic Skeleton-Based Hand Gesture and Human Action Recognition—0
The Impact of Code-switched Synthetic Data Quality is Task Dependent: Insights from MT and ASR—0
The Impact of Frequency Bands on Acoustic Anomaly Detection of Machines using Deep Learning Based Model—0
The Impact of Preprocessing on Deep Representations for Iris Recognition on Unconstrained Environments—0
The Importance of Importance Sampling for Deep Budgeted Training—0
The Influences of Color and Shape Features in Visual Contrastive Learning—0
The JHU-Microsoft Submission for WMT21 Quality Estimation Shared Task—0
The LMU Munich System for the WMT 2021 Large-Scale Multilingual Machine Translation Shared Task—0
The LMU System for the CoNLL-SIGMORPHON 2017 Shared Task on Universal Morphological Reinflection—0
The Meaning Factory at SemEval-2017 Task 9: Producing AMRs with Neural Semantic Parsing—0
The MLLP-UPV German-English Machine Translation System for WMT18—0
The NIST CTS Speaker Recognition Challenge—0
The Notary in the Haystack -- Countering Class Imbalance in Document Processing with CNNs—0
The NTNU System at the Interspeech 2020 Non-Native Children's Speech ASR Challenge—0
The NTNU Taiwanese ASR System for Formosa Speech Recognition Challenge 2020—0
The NUS-HLT System for ICASSP2024 ICMC-ASR Grand Challenge—0
The Only Chance to Understand: Machine Translation of the Severely Endangered Low-resource Languages of Eurasia—0
Theoretical Analysis of Consistency Regularization with Limited Augmented Data—0
Theoretical and Empirical Study of Adversarial Examples—0
Theoretical Guarantees of Data Augmented Last Layer Retraining Methods—0
The Outcome of the 2022 Landslide4Sense Competition: Advanced Landslide Detection from Multi-Source Satellite Imagery—0
The Penalty Imposed by Ablated Data Augmentation—0
The Perception of Phase Intercept Distortion and its Application in Data Augmentation—0
The Pipeline System of ASR and NLU with MLM-based Data Augmentation toward STOP Low-resource Challenge—0
The Potential of Neural Speech Synthesis-based Data Augmentation for Personalized Speech Enhancement—0
The Quest for Efficient Reasoning: A Data-Centric Benchmark to CoT Distillation—0
Learning ABCs: Approximate Bijective Correspondence for isolating factors of variation with weak supervision—0
Thermal-Infrared Remote Target Detection System for Maritime Rescue based on Data Augmentation with 3D Synthetic Data—0
The RoboDrive Challenge: Drive Anytime Anywhere in Any Condition—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