SOTAVerified

Transfer Learning

Transfer Learning is a machine learning technique where a model trained on one task is re-purposed and fine-tuned for a related, but different task. The idea behind transfer learning is to leverage the knowledge learned from a pre-trained model to solve a new, but related problem. This can be useful in situations where there is limited data available to train a new model from scratch, or when the new task is similar enough to the original task that the pre-trained model can be adapted to the new problem with only minor modifications.

( Image credit: Subodh Malgonde )

Papers

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Showing 2401–2425 of 10307 papers

TitleStatusHype
BIOWISH: Biometric Recognition using Wearable Inertial Sensors detecting Heart Activity—0
A Deep Q-Network for the Beer Game: A Deep Reinforcement Learning algorithm to Solve Inventory Optimization Problems—0
Deep Learning Techniques for Hand Vein Biometrics: A Comprehensive Review—0
A New Channel Boosted Convolutional Neural Network using Transfer Learning—0
BioNCERE: Non-Contrastive Enhancement For Relation Extraction In Biomedical Texts—0
Biometric Fish Classification of Temperate Species Using Convolutional Neural Network with Squeeze-and-Excitation—0
A Deep Learning System That Generates Quantitative CT Reports for Diagnosing Pulmonary Tuberculosis—0
Bio-Measurements Estimation and Support in Knee Recovery through Machine Learning—0
Biological neurons act as generalization filters in reservoir computing—0
A Deep Learning Study on Osteosarcoma Detection from Histological Images—0
FE-Adapter: Adapting Image-based Emotion Classifiers to Videos—0
Transfer Learning with Edge Attention for Prostate MRI Segmentation—0
Biologically inspired sleep algorithm for artificial neural networks—0
BioIE: Biomedical Information Extraction with Multi-head Attention Enhanced Graph Convolutional Network—0
BioBERTpt - A Portuguese Neural Language Model for Clinical Named Entity Recognition—0
BioAMA: Towards an End to End BioMedical Question Answering System—0
An Evaluation of Recent Neural Sequence Tagging Models in Turkish Named Entity Recognition—0
A Deep Learning Sequential Decoder for Transient High-Density Electromyography in Hand Gesture Recognition Using Subject-Embedded Transfer Learning—0
Deep learning segmentation of fibrous cap in intravascular optical coherence tomography images—0
Deep LSTM for Large Vocabulary Continuous Speech Recognition—0
An Evaluation of Progressive Neural Networksfor Transfer Learning in Natural Language Processing—0
BiNet: Degraded-Manuscript Binarization in Diverse Document Textures and Layouts using Deep Encoder-Decoder Networks—0
A Deep Learning Perspective on the Origin of Facial Expressions—0
Binary Paragraph Vectors—0
Binary Classification of Alzheimer Disease using sMRI Imaging modality and Deep Learning—0
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All datasetsOffice-Home100 sleep nights of 8 caregiversBanglaLekha Isolated DatasetCOCO70KITTI Object Tracking Evaluation 2012Retinal Fundus MultiDisease Image Dataset (RFMiD)

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1APCLIPAccuracy84.2—Unverified
2DFA-ENTAccuracy69.2—Unverified
3DFA-SAFNAccuracy69.1—Unverified
4EasyTLAccuracy63.3—Unverified
5MEDAAccuracy60.3—Unverified
#ModelMetricClaimedVerifiedStatus
1CNN10-20% Mask PSNR3.23—Unverified
#ModelMetricClaimedVerifiedStatus
1Chatterjee, Dutta et al.[1]Accuracy96.12—Unverified
#ModelMetricClaimedVerifiedStatus
1Co-TuningAccuracy85.65—Unverified
#ModelMetricClaimedVerifiedStatus
1Physical AccessEER5.74—Unverified
#ModelMetricClaimedVerifiedStatus
1riadd.aucmediAUROC0.95—Unverified