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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2151–2175 of 10307 papers

TitleStatusHype
A synthetic data approach for domain generalization of NLI models—0
Spatio-Temporal Few-Shot Learning via Diffusive Neural Network GenerationCode2
Key ingredients for effective zero-shot cross-lingual knowledge transfer in generative tasks—0
Mitigating Catastrophic Forgetting in Multi-domain Chinese Spelling Correction by Multi-stage Knowledge Transfer Framework—0
LEIA: Facilitating Cross-lingual Knowledge Transfer in Language Models with Entity-based Data AugmentationCode1
Autocorrect for Estonian texts: final report from project EKTB25—0
ZeroG: Investigating Cross-dataset Zero-shot Transferability in GraphsCode1
A Question Answering Based Pipeline for Comprehensive Chinese EHR Information Extraction—0
An end-to-end attention-based approach for learning on graphsCode2
Differential Private Federated Transfer Learning for Mental Health Monitoring in Everyday Settings: A Case Study on Stress Detection—0
Robust agents learn causal world models—0
Personalised Drug Identifier for Cancer Treatment with Transformers using Auxiliary InformationCode0
Multiview Contrastive Learning for Unsupervised Domain Adaptation in Brain–Computer Interfaces—0
BrainWave: A Brain Signal Foundation Model for Clinical ApplicationsCode1
Towards Precision Cardiovascular Analysis in Zebrafish: The ZACAF Paradigm—0
Data Augmentation and Transfer Learning Approaches Applied to Facial Expressions Recognition—0
How does Your RL Agent Explore? An Optimal Transport Analysis of Occupancy Measure Trajectories—0
Multi-Hierarchical Surrogate Learning for Structural Dynamical Crash Simulations Using Graph Convolutional Neural Networks—0
Subgraph Pooling: Tackling Negative Transfer on GraphsCode0
Evaluation of Activated Sludge Settling Characteristics from Microscopy Images with Deep Convolutional Neural Networks and Transfer LearningCode0
Few-Shot Object Detection with Sparse Context Transformers—0
Enabling Multi-Agent Transfer Reinforcement Learning via Scenario Independent Representation—0
Bayesian Multi-Task Transfer Learning for Soft Prompt TuningCode0
FedLPS: Heterogeneous Federated Learning for Multiple Tasks with Local Parameter SharingCode1
Convolutional Neural Networks Towards Facial Skin Lesions Detection—0
Show:102550
← PrevPage 87 of 413Next →
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