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 6251–6300 of 8378 papers

TitleStatusHype
Recognising Biomedical Names: Challenges and Solutions—0
Recurrent Coupled Topic Modeling over Sequential Documents—0
Making Invisible Visible: Data-Driven Seismic Inversion with Spatio-temporally Constrained Data Augmentation—0
Data Augmentation for Opcode Sequence Based Malware Detection—0
Obstacle Detection for BVLOS Drones—0
Learning to Rank Question Answer Pairs with Bilateral Contrastive Data Augmentation—0
Ensemble of ACCDOA- and EINV2-based Systems with D3Nets and Impulse Response Simulation for Sound Event Localization and Detection—0
Customizing Graph Neural Networks using Path ReweightingCode0
Does Robustness Improve Fairness? Approaching Fairness with Word Substitution Robustness Methods for Text Classification—0
Quality-Agnostic Image Recognition via Invertible DecoderCode0
LiDAR-Aug: A General Rendering-Based Augmentation Framework for 3D Object Detection—0
Learning a Facial Expression Embedding Disentangled From Identity—0
MaxUp: Lightweight Adversarial Training With Data Augmentation Improves Neural Network Training—0
PointAugmenting: Cross-Modal Augmentation for 3D Object Detection—0
Novelty Detection via Contrastive Learning with Negative Data Augmentation—0
Virtual Temporal Samples for Recurrent Neural Networks: applied to semantic segmentation in agriculture—0
Investigating the Role of Negatives in Contrastive Representation Learning—0
Toward Fault Detection in Industrial Welding Processes with Deep Learning and Data Augmentation—0
Low Resource German ASR with Untranscribed Data Spoken by Non-native Children -- INTERSPEECH 2021 Shared Task SPAPL System—0
Joining datasets via data augmentation in the label space for neural networks—0
Deep HDR Hallucination for Inverse Tone Mapping—0
Can I Be of Further Assistance? Using Unstructured Knowledge Access to Improve Task-oriented Conversational Modeling—0
Data Augmentation for Graph Convolutional Network on Semi-Supervised Classification—0
ParticleAugment: Sampling-Based Data Augmentation—0
Best Practices for Noise-Based Augmentation to Improve the Performance of Deployable Speech-Based Emotion Recognition Systems—0
Effective Evaluation of Deep Active Learning on Image Classification Tasks—0
Evolving Image Compositions for Feature Representation Learning—0
Optimizing Data Augmentation Policy Through Random Unidimensional SearchCode0
SRIB Submission to Interspeech 2021 DiCOVA Challenge—0
Mixed Model OCR Training on Historical Latin Script for Out-of-the-Box Recognition and Finetuning—0
CAN-LOC: Spoofing Detection and Physical Intrusion Localization on an In-Vehicle CAN Bus Based on Deep Features of Voltage Signals—0
CT Image Synthesis Using Weakly Supervised Segmentation and Geometric Inter-Label Relations For COVID Image Analysis—0
Mean Embeddings with Test-Time Data Augmentation for Ensembling of Representations—0
Generating Data Augmentation samples for Semantic Segmentation of Salt Bodies in a Synthetic Seismic Image Dataset—0
Cluster-guided Asymmetric Contrastive Learning for Unsupervised Person Re-IdentificationCode0
SAS: Self-Augmentation Strategy for Language Model Pre-trainingCode0
Last Layer Marginal Likelihood for Invariance LearningCode0
End-to-end Neural Diarization: From Transformer to Conformer—0
SynthASR: Unlocking Synthetic Data for Speech Recognition—0
An Empirical Survey of Data Augmentation for Limited Data Learning in NLP—0
Survey: Image Mixing and Deleting for Data AugmentationCode0
Go Small and Similar: A Simple Output Decay Brings Better Performance—0
EPICURE Ensemble Pretrained Models for Extracting Cancer Mutations from Literature—0
Efficient Deep Learning Architectures for Fast Identification of Bacterial Strains in Resource-Constrained DevicesCode0
Disentangling the Roles of Curation, Data-Augmentation and the Prior in the Cold Posterior Effect—0
Data augmentation in Bayesian neural networks and the cold posterior effect—0
U2++: Unified Two-pass Bidirectional End-to-end Model for Speech Recognition—0
Relational Data Selection for Data Augmentation of Speaker-dependent Multi-band MelGAN Vocoder—0
Data augmentation to improve robustness of image captioning solutions—0
A Comparative Study on Neural Architectures and Training Methods for Japanese Speech Recognition—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