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 6226–6250 of 10307 papers

TitleStatusHype
A relic sketch extraction framework based on detail-aware hierarchical deep network—0
Neuronal and structural differentiation in the emergence of abstract rules in hierarchically modulated spiking neural networks—0
A Recurrent Neural Network Approach to the Answering Machine Detection Problem—0
A Real Use Case of Semi-Supervised Learning for Mammogram Classification in a Local Clinic of Costa Rica—0
Self-supervised similarity models based on well-logging data—0
Hyperspectral Image Restoration and Super-resolution with Physics-Aware Deep Learning for Biomedical Applications—0
Hyperspectral Imaging Technology and Transfer Learning Utilized in Identification Haploid Maize Seeds—0
HyperspectralMAE: The Hyperspectral Imagery Classification Model using Fourier-Encoded Dual-Branch Masked Autoencoder—0
Hyperspectral Pixel Unmixing with Latent Dirichlet Variational Autoencoder—0
Arabic Text Diacritization In The Age Of Transfer Learning: Token Classification Is All You Need—0
Hypothesis Disparity Regularized Mutual Information Maximization—0
A Concise Review of Transfer Learning—0
Hypothesis Transfer in Bandits by Weighted Models—0
Hypothesis Transfer Learning via Transformation Functions—0
Hypothesis Transfer Learning with Surrogate Classification Losses: Generalization Bounds through Algorithmic Stability—0
I2CKD : Intra- and Inter-Class Knowledge Distillation for Semantic Segmentation—0
A Question Answering Based Pipeline for Comprehensive Chinese EHR Information Extraction—0
I2T2I: Learning Text to Image Synthesis with Textual Data Augmentation—0
A QUBO Framework for Team Formation—0
A Quantum Neural Network Transfer-Learning Model for Forecasting Problems with Continuous and Discrete Variables—0
ICGNN: Graph Neural Network Enabled Scalable Beamforming for MISO Interference Channels—0
A deep learning-enabled smart garment for accurate and versatile sleep conditions monitoring in daily life—0
iConFormer: Dynamic Parameter-Efficient Tuning with Input-Conditioned Adaptation—0
Intelligent Model Update Strategy for Sequential Recommendation—0
Identification of Cognitive Workload during Surgical Tasks with Multimodal 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