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 6101–6150 of 8378 papers

TitleStatusHype
CASIA at SemEval-2022 Task 11: Chinese Named Entity Recognition for Complex and Ambiguous Entities—0
Catastrophic Child's Play: Easy to Perform, Hard to Defend Adversarial Attacks—0
Catch-Up Mix: Catch-Up Class for Struggling Filters in CNN—0
CAT-Det: Contrastively Augmented Transformer for Multi-modal 3D Object Detection—0
Category-Learning with Context-Augmented Autoencoder—0
Category-Level 6D Object Pose Estimation with Flexible Vector-Based Rotation Representation—0
CausalDyna: Improving Generalization of Dyna-style Reinforcement Learning via Counterfactual-Based Data Augmentation—0
Causal Information Prioritization for Efficient Reinforcement Learning—0
Interpreting the Robustness of Neural NLP Models to Textual Perturbations—0
Causal Prompting: Debiasing Large Language Model Prompting based on Front-Door Adjustment—0
Causal Reasoning: Charting a Revolutionary Course for Next-Generation AI-Native Wireless Networks—0
Data Augmentations for Improved (Large) Language Model Generalization—0
CAUSE: Counterfactual Assessment of User Satisfaction Estimation in Task-Oriented Dialogue Systems—0
CCFace: Classification Consistency for Low-Resolution Face Recognition—0
CDS: Data Synthesis Method Guided by Cognitive Diagnosis Theory—0
CDUPatch: Color-Driven Universal Adversarial Patch Attack for Dual-Modal Visible-Infrared Detectors—0
CEKD:Cross Ensemble Knowledge Distillation for Augmented Fine-grained Data—0
Center-aware Adversarial Augmentation for Single Domain Generalization—0
Center-wise Local Image Mixture For Contrastive Representation Learning—0
Certified Adversarial Defenses Meet Out-of-Distribution Corruptions: Benchmarking Robustness and Simple Baselines—0
Certifying Adapters: Enabling and Enhancing the Certification of Classifier Adversarial Robustness—0
Read and Think: An Efficient Step-wise Multimodal Language Model for Document Understanding and Reasoning—0
Chain of History: Learning and Forecasting with LLMs for Temporal Knowledge Graph Completion—0
Chain-of-Thought Prompt Distillation for Multimodal Named Entity Recognition and Multimodal Relation Extraction—0
Challenges and Limitations in the Synthetic Generation of mHealth Sensor Data—0
Challenges and Opportunities for Machine Learning Classification of Behavior and Mental State from Images—0
ChannelAugment: Improving generalization of multi-channel ASR by training with input channel randomization—0
Channel Modeling Aided Dataset Generation for AI-Enabled CSI Feedback: Advances, Challenges, and Solutions—0
Channel Reflection: Knowledge-Driven Data Augmentation for EEG-Based Brain-Computer Interfaces—0
ChapGTP, ILLC's Attempt at Raising a BabyLM: Improving Data Efficiency by Automatic Task Formation—0
Character-Based Models for Adversarial Phone Extraction: Preventing Human Sex Trafficking—0
Characterizing Speech Adversarial Examples Using Self-Attention U-Net Enhancement—0
Character-level Chinese Writer Identification using Path Signature Feature, DropStroke and Deep CNN—0
Characters Detection on Namecard with faster RCNN—0
Chat-based Person Retrieval via Dialogue-Refined Cross-Modal Alignment—0
Chatbot Interaction with Artificial Intelligence: Human Data Augmentation with T5 and Language Transformer Ensemble for Text Classification—0
ChatGPT for Zero-shot Dialogue State Tracking: A Solution or an Opportunity?—0
Checks and Strategies for Enabling Code-Switched Machine Translation—0
Check-worthy Claim Detection across Topics for Automated Fact-checking—0
Chinese Grammatical Error Correction Based on Hybrid Models with Data Augmentation—0
Choreographing the Digital Canvas: A Machine Learning Approach to Artistic Performance—0
CILDA: Contrastive Data Augmentation using Intermediate Layer Knowledge Distillation—0
CIPER: Combining Invariant and Equivariant Representations Using Contrastive and Predictive Learning—0
CK4Gen: A Knowledge Distillation Framework for Generating High-Utility Synthetic Survival Datasets in Healthcare—0
CKMDiff: A Generative Diffusion Model for CKM Construction via Inverse Problems with Learned Priors—0
Self-supervised New Activity Detection in Sensor-based Smart Environments—0
Clarifying Myths About the Relationship Between Shape Bias, Accuracy, and Robustness—0
CLASP: Few-Shot Cross-Lingual Data Augmentation for Semantic Parsing—0
Class-Aware PillarMix: Can Mixed Sample Data Augmentation Enhance 3D Object Detection with Radar Point Clouds?—0
Class-Aware Universum Inspired Re-Balance Learning for Long-Tailed 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 2×96d) (Faster AA)Percentage error2—Unverified
5Shake-Shake (26 2×112d) (Faster AA)Percentage error2—Unverified
#ModelMetricClaimedVerifiedStatus
1DiffAugClassification Accuracy92.7—Unverified
2PaCMAPClassification Accuracy85.3—Unverified
3hNNEClassification Accuracy77.4—Unverified
4TopoAEClassification Accuracy74.6—Unverified