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 6551–6600 of 8378 papers

TitleStatusHype
Multistream CNN for Robust Acoustic Modeling—0
Multi-style Training for South African Call Centre Audio—0
Multitask-Based Joint Learning Approach To Robust ASR For Radio Communication Speech—0
Multi-Task Distribution Learning—0
Multitask frame-level learning for few-shot sound event detection—0
Multitask Learning On Graph Neural Networks Applied To Molecular Property Predictions—0
Reducing the Model Variance of a Rectal Cancer Segmentation Network—0
Multi tasks RetinaNet for mitosis detection—0
Multi-Task Zero-Shot Action Recognition with Prioritised Data Augmentation—0
Multi-VALUE: A Framework for Cross-Dialectal English NLP—0
Multi-view Contrastive Self-Supervised Learning of Accounting Data Representations for Downstream Audit Tasks—0
Multi-View Correlation Consistency for Semi-Supervised Semantic Segmentation—0
Multi-View Incongruity Learning for Multimodal Sarcasm Detection—0
MultiViT2: A Data-augmented Multimodal Neuroimaging Prediction Framework via Latent Diffusion Model—0
Multi-Window Data Augmentation Approach for Speech Emotion Recognition—0
Multiword Expression aware Neural Machine Translation—0
Music Playlist Title Generation: A Machine-Translation Approach—0
Music Source Separation in the Waveform Domain—0
Music Transcription by Deep Learning with Data and "Artificial Semantic" Augmentation—0
Mutual Information Learned Classifiers: an Information-theoretic Viewpoint of Training Deep Learning Classification Systems—0
MVAD: A Multiple Visual Artifact Detector for Video Streaming—0
MVP: Meta Visual Prompt Tuning for Few-Shot Remote Sensing Image Scene Classification—0
MVTec D2S: Densely Segmented Supermarket Dataset—0
MYCROFT: Towards Effective and Efficient External Data Augmentation—0
My Emotion on your face: The use of Facial Keypoint Detection to preserve Emotions in Latent Space Editing—0
MyoPS: A Benchmark of Myocardial Pathology Segmentation Combining Three-Sequence Cardiac Magnetic Resonance Images—0
Named Entity Recognition in Industrial Tables using Tabular Language Models—0
NAP at SemEval-2023 Task 3: Is Less Really More? (Back-)Translation as Data Augmentation Strategies for Detecting Persuasion Techniques—0
Narrowing Class-Wise Robustness Gaps in Adversarial Training—0
NAS-Cap: Deep-Learning Driven 3-D Capacitance Extraction with Neural Architecture Search and Data Augmentation—0
Natural language understanding for task oriented dialog in the biomedical domain in a low resources context—0
Neighborhood Watch: Representation Learning with Local-Margin Triplet Loss and Sampling Strategy for K-Nearest-Neighbor Image Classification—0
NeRF-Aug: Data Augmentation for Robotics with Neural Radiance Fields—0
NeRF in the Palm of Your Hand: Corrective Augmentation for Robotics via Novel-View Synthesis—0
NeRFmentation: NeRF-based Augmentation for Monocular Depth Estimation—0
NetSentry: A Deep Learning Approach to Detecting Incipient Large-scale Network Attacks—0
Network Augmentation for Tiny Deep Learning—0
NEU at WNUT-2020 Task 2: Data Augmentation To Tell BERT That Death Is Not Necessarily Informative—0
Neural Activation Constellations: Unsupervised Part Model Discovery with Convolutional Networks—0
Neural Algorithmic Reasoning with Causal Regularisation—0
Neural Collaborative Autoencoder—0
Neural Data-to-Text Generation Based on Small Datasets: Comparing the Added Value of Two Semi-Supervised Learning Approaches on Top of a Large Language Model—0
Neural Fuzzy Repair: Integrating Fuzzy Matches into Neural Machine Translation—0
Neural Machine Translation: A Review of Methods, Resources, and Tools—0
Neural model robustness for skill routing in large-scale conversational AI systems: A design choice exploration—0
Neural Models for Source Code Synthesis and Completion—0
Neural Network Based Lidar Gesture Recognition for Realtime Robot Teleoperation—0
Neural Networks Preserve Invertibility Across Iterations: A Possible Source of Implicit Data Augmentation—0
Neural Network Tomography—0
Neural network with data augmentation in multi-objective prediction of multi-stage pump—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