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 1526–1550 of 10307 papers

TitleStatusHype
Automated Web-Based Malaria Detection System with Machine Learning and Deep Learning TechniquesCode0
Automated Web-Based Malaria Detection System with Machine Learning and Deep Learning Techniques: A Comparative AnalysisCode0
Towards Learning Abductive Reasoning using VSA Distributed RepresentationsCode1
T-FREE: Subword Tokenizer-Free Generative LLMs via Sparse Representations for Memory-Efficient EmbeddingsCode2
RouteLLM: Learning to Route LLMs with Preference DataCode7
Learn it or Leave it: Module Composition and Pruning for Continual Learning—0
VIPriors 4: Visual Inductive Priors for Data-Efficient Deep Learning Challenges—0
MoE-CT: A Novel Approach For Large Language Models Training With Resistance To Catastrophic Forgetting—0
LABOR-LLM: Language-Based Occupational Representations with Large Language Models—0
BMIKE-53: Investigating Cross-Lingual Knowledge Editing with In-Context Learning—0
WAVE: Weight Template for Adaptive Initialization of Variable-sized Models—0
Leveraging Parameter-Efficient Transfer Learning for Multi-Lingual Text-to-Speech Adaptation—0
Hyperbolic Knowledge Transfer in Cross-Domain Recommendation System—0
Robust NLoS Localization in 5G mmWave Networks: Data-based Methods and Performance—0
Convolutional neural network for Lyman break galaxies classification and redshift regression in DESI (Dark Energy Spectroscopic Instrument)—0
When Invariant Representation Learning Meets Label Shift: Insufficiency and Theoretical Insights—0
Federated Transfer Learning Aided Interference Classification in GNSS Signals—0
Accelerating Matrix Diagonalization through Decision Transformers with Epsilon-Greedy Optimization—0
Crosslingual Capabilities and Knowledge Barriers in Multilingual Large Language ModelsCode1
Evaluation and Comparison of Emotionally Evocative Image Augmentation Methods—0
Bone Fracture Classification using Transfer LearningCode0
Flat Posterior Does Matter For Bayesian Model AveragingCode0
Multi-Domain Evolutionary Optimization of Network Structures—0
GOAL: A Generalist Combinatorial Optimization Agent LearningCode1
Learning to Transfer for Evolutionary Multitasking—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