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 10176–10200 of 10307 papers

TitleStatusHype
Semi-Supervised Learning with Scarce AnnotationsCode0
Semi-supervised Multimodal Representation Learning through a Global WorkspaceCode0
Cross-hospital Sepsis Early Detection via Semi-supervised Optimal Transport with Self-paced EnsembleCode0
Semi-unsupervised Learning of Human Activity using Deep Generative ModelsCode0
Semi-weakly Supervised Contrastive Representation Learning for Retinal Fundus ImagesCode0
Sentence Encoders on STILTs: Supplementary Training on Intermediate Labeled-data TasksCode0
Sentiment analysis in Bengali via transfer learning using multi-lingual BERTCode0
SeqNet: Sequential Networks for One-Shot Traffic Sign Recognition With Transfer LearningCode0
Sequential Order-Robust Mamba for Time Series ForecastingCode0
Overcoming Concept Shift in Domain-Aware Settings through Consolidated Internal DistributionsCode0
SEQUENT: Towards Traceable Quantum Machine Learning using Sequential Quantum Enhanced TrainingCode0
A Theoretical Understanding of Gradient Bias in Meta-Reinforcement LearningCode0
SexWEs: Domain-Aware Word Embeddings via Cross-lingual Semantic Specialisation for Chinese Sexism Detection in Social MediaCode0
SFS-A68: a dataset for the segmentation of space functions in apartment buildingsCode0
SHAMSUL: Systematic Holistic Analysis to investigate Medical Significance Utilizing Local interpretability methods in deep learning for chest radiography pathology predictionCode0
Shapechanger: Environments for Transfer LearningCode0
ShareLoRA: Parameter Efficient and Robust Large Language Model Fine-tuning via Shared Low-Rank AdaptationCode0
SHERL: Synthesizing High Accuracy and Efficient Memory for Resource-Limited Transfer LearningCode0
SHiFT: An Efficient, Flexible Search Engine for Transfer LearningCode0
SimbaML: Connecting Mechanistic Models and Machine Learning with Augmented DataCode0
SimCPSR: Simple Contrastive Learning for Paper Submission Recommendation SystemCode0
simCrossTrans: A Simple Cross-Modality Transfer Learning for Object Detection with ConvNets or Vision TransformersCode0
Simple and Effective Zero-shot Cross-lingual Phoneme RecognitionCode0
Simple Transferability Estimation for Regression TasksCode0
Simplified Learning of CAD Features Leveraging a Deep Residual AutoencoderCode0
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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