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 3051–3075 of 10307 papers

TitleStatusHype
Mixture of Latent Experts Using Tensor Products—0
Daily Physical Activity Monitoring -- Adaptive Learning from Multi-source Motion Sensor Data—0
Image-Text-Image Knowledge Transferring for Lifelong Person Re-Identification with Hybrid Clothing States—0
Noisy Data Meets Privacy: Training Local Models with Post-Processed Remote Queries—0
Generation of synthetic data using breast cancer dataset and classification with resnet18—0
A transfer learning framework for weak-to-strong generalization—0
SF-DQN: Provable Knowledge Transfer using Successor Feature for Deep Reinforcement Learning—0
Environment Sensing-aided Beam Prediction with Transfer Learning for Smart Factory—0
Uncovering cognitive taskonomy through transfer learning in masked autoencoder-based fMRI reconstruction—0
Disease-informed Adaptation of Vision-Language ModelsCode0
Detection and Positive Reconstruction of Cognitive Distortion sentences: Mandarin Dataset and Evaluation—0
Transfer Learning with Informative Priors: Simple Baselines Better than Previously ReportedCode0
The Impact of Geometric Complexity on Neural Collapse in Transfer Learning—0
Combining Denoising Autoencoders with Contrastive Learning to fine-tune Transformer ModelsCode0
SolNet: Open-source deep learning models for photovoltaic power forecasting across the globe—0
An LSTM Feature Imitation Network for Hand Movement Recognition from sEMG SignalsCode0
Federated Domain-Specific Knowledge Transfer on Large Language Models Using Synthetic Data—0
Magnetic Resonance Image Processing Transformer for General Accelerated Image Reconstruction—0
Deep learning lattice gauge theories—0
MAMOC: MRI Motion Correction via Masked Autoencoding—0
CrossVoice: Crosslingual Prosody Preserving Cascade-S2ST using Transfer Learning—0
Multilingual Prosody Transfer: Comparing Supervised & Transfer Learning—0
Dynamically enhanced static handwriting representation for Parkinson's disease detection—0
Transfer of Safety Controllers Through Learning Deep Inverse Dynamics Model—0
Multi-Dataset Multi-Task Learning for COVID-19 Prognosis—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