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:

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Papers

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Showing 6251–6300 of 8378 papers

TitleStatusHype
Conditional Generative Data Augmentation for Clinical Audio Datasets—0
Conditionally-Conjugate Gaussian Process Factor Analysis for Spike Count Data via Data Augmentation—0
Conditional Semi-Supervised Data Augmentation for Spam Message Detection with Low Resource Data—0
Conditional set generation using Seq2seq models—0
Conditional Synthetic Data Generation for Robust Machine Learning Applications with Limited Pandemic Data—0
Conditional Synthetic Food Image Generation—0
Unified Framework for Histopathology Image Augmentation and Classification via Generative Models—0
Confidence-Guided Data Augmentation for Improved Semi-Supervised Training—0
Configuring Data Augmentations to Reduce Variance Shift in Positional Embedding of Vision Transformers—0
ConFit: Improving Resume-Job Matching using Data Augmentation and Contrastive Learning—0
Conformer Based Elderly Speech Recognition System for Alzheimer's Disease Detection—0
CoNLL-SIGMORPHON 2017 Shared Task: Universal Morphological Reinflection in 52 Languages—0
Consecutive Question Generation via Dynamic Multitask Learning—0
Consensus Clustering With Unsupervised Representation Learning—0
Consistency and Monotonicity Regularization for Neural Knowledge Tracing—0
Consistent Text Categorization using Data Augmentation in e-Commerce—0
CONTEMPLATING REAL-WORLDOBJECT RECOGNITION—0
Content-Conditioned Generation of Stylized Free hand Sketches—0
Context-aware Adversarial Training for Name Regularity Bias in Named Entity Recognition—0
Context-Aware Attention-Based Data Augmentation for POI Recommendation—0
Context-Aware Data Augmentation for LIDAR 3D Object Detection—0
Context-Aware Language Modeling for Goal-Oriented Dialogue Systems—0
Context-gloss Augmentation for Improving Word Sense Disambiguation—0
Context-Preserving Instance-Level Augmentation and Deformable Convolution Networks for SAR Ship Detection—0
Contextual Data Augmentation for Task-Oriented Dialog Systems—0
Contextual Scene Augmentation and Synthesis via GSACNet—0
Continual Few-shot Relation Learning via Embedding Space Regularization and Data Augmentation—0
Continual Unsupervised Domain Adaptation for Semantic Segmentation using a Class-Specific Transfer—0
Continuing Pre-trained Model with Multiple Training Strategies for Emotional Classification—0
Continuous 3D Multi-Channel Sign Language Production via Progressive Transformers and Mixture Density Networks—0
Continuous Control Reinforcement Learning: Distributed Distributional DrQ Algorithms—0
Contrastive Fine-tuning Improves Robustness for Neural Rankers—0
ContraGAN: Contrastive Learning for Conditional Image Generation—0
Contrastive Learning as Goal-Conditioned Reinforcement Learning—0
Contrastive Learning for Context-aware Neural Machine TranslationUsing Coreference Information—0
Contrastive Learning for Context-aware Neural Machine Translation Using Coreference Information—0
Contrastive Learning for Low Resource Machine Translation—0
Towards Learning (Dis)-Similarity of Source Code from Program Contrasts—0
Contrastive learning for unsupervised medical image clustering and reconstruction—0
Contrastive Learning for Unsupervised Radar Place Recognition—0
Contrastive Learning from Pairwise Measurements—0
Contrastive Learning is Just Meta-Learning—0
Contrastive Learning with Negative Sampling Correction—0
Contrastive-mixup learning for improved speaker verification—0
Pre-training General Trajectory Embeddings with Maximum Multi-view Entropy Coding—0
Contrastive Psudo-supervised Classification for Intra-Pulse Modulation of Radar Emitter Signals Using data augmentation—0
Contrastive Representation Learning for Acoustic Parameter Estimation—0
Contrastive Self-supervised Learning for Graph Classification—0
Contrastive Unsupervised Learning of World Model with Invariant Causal Features—0
Contrastive Visual Data Augmentation—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