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 4851–4900 of 8378 papers

TitleStatusHype
Transductive Label Augmentation for Improved Deep Network Learning—0
Transesophageal Echocardiography Generation using Anatomical Models—0
Transferable Natural Language Interface to Structured Queries aided by Adversarial Generation—0
Transferable Unsupervised Robust Representation Learning—0
Transfer Incremental Learning using Data Augmentation—0
Transfer Learning and Augmentation for Word Sense Disambiguation—0
Transfer Learning and SpecAugment applied to SSVEP Based BCI Classification—0
Transfer Learning for Oral Cancer Detection using Microscopic Images—0
Transfer Learning for Robust Low-Resource Children's Speech ASR with Transformers and Source-Filter Warping—0
Transfer Learning on Manifolds via Learned Transport Operators—0
Transfer Learning with Deep CNNs for Gender Recognition and Age Estimation—0
Transferring a molecular foundation model for polymer property predictions—0
Transferring Modality-Aware Pedestrian Attentive Learning for Visible-Infrared Person Re-identification—0
Transformationally Identical and Invariant Convolutional Neural Networks through Symmetric Element Operators—0
Transformation-Equivariant 3D Object Detection for Autonomous Driving—0
RIE-SenseNet: Riemannian Manifold Embedding of Multi-Source Industrial Sensor Signals for Robust Pattern Recognition—0
Transformer-based Capacity Prediction for Lithium-ion Batteries with Data Augmentation—0
Transformer-based Multimodal Information Fusion for Facial Expression Analysis—0
Transformer with Bidirectional Decoder for Speech Recognition—0
Transformer with Selective Shuffled Position Embedding and Key-Patch Exchange Strategy for Early Detection of Knee Osteoarthritis—0
Transforming Wikipedia into Augmented Data for Query-Focused Summarization—0
TransformMix: Learning Transformation and Mixing Strategies from Data—0
Deep Fusion: Capturing Dependencies in Contrastive Learning via Transformer Projection Heads—0
Translate & Fill: Improving Zero-Shot Multilingual Semantic Parsing with Synthetic Data—0
Translation Deserves Better: Analyzing Translation Artifacts in Cross-lingual Visual Question Answering—0
Translatotron 2: High-quality direct speech-to-speech translation with voice preservation—0
A Novel Transparency Strategy-based Data Augmentation Approach for BI-RADS Classification of Mammograms—0
Transplantation of Conversational Speaking Style with Interjections in Sequence-to-Sequence Speech Synthesis—0
TransSGAN: GAN based semi-superivsed learning for text classification with Transformer Encoder—0
TranssionADD: A multi-frame reinforcement based sequence tagging model for audio deepfake detection—0
TreeFormers -- An Exploration of Vision Transformers for Deforestation Driver Classification—0
Triangular Contrastive Learning on Molecular Graphs—0
Virtual embeddings and self-consistency for self-supervised learning—0
Triple-GAIL: A Multi-Modal Imitation Learning Framework with Generative Adversarial Nets—0
Triplet-Aware Scene Graph Embeddings—0
Triplet Contrastive Learning for Brain Tumor Classification—0
MM-Mixing: Multi-Modal Mixing Alignment for 3D Understanding—0
Trustworthiness of Laser-Induced Breakdown Spectroscopy Predictions via Simulation-based Synthetic Data Augmentation and Multitask Learning—0
Trustworthy image-to-image translation: evaluating uncertainty calibration in unpaired training scenarios—0
Truth or Twist? Optimal Model Selection for Reliable Label Flipping Evaluation in LLM-based Counterfactuals—0
TSMind: Alibaba and Soochow University's Submission to the WMT22 Translation Suggestion Task—0
TSV Extrusion Morphology Classification Using Deep Convolutional Neural Networks—0
Tube-NeRF: Efficient Imitation Learning of Visuomotor Policies from MPC using Tube-Guided Data Augmentation and NeRFs—0
Tubule segmentation of fluorescence microscopy images based on convolutional neural networks with inhomogeneity correction—0
TULIP: Towards Unified Language-Image Pretraining—0
TumorNet: Lung Nodule Characterization Using Multi-View Convolutional Neural Network with Gaussian Process—0
TuneUp: A Simple Improved Training Strategy for Graph Neural Networks—0
Tuning Mixed Input Hyperparameters on the Fly for Efficient Population Based AutoRL—0
T\"upa at SemEval-2019 Task1: (Almost) feature-free Semantic Parsing—0
Twin Systems for DeepCBR: A Menagerie of Deep Learning and Case-Based Reasoning Pairings for Explanation and 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