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 451–475 of 10307 papers

TitleStatusHype
E(3)-Equivariant Actor-Critic Methods for Cooperative Multi-Agent Reinforcement LearningCode1
Efficient and Flexible Neural Network Training through Layer-wise Feedback PropagationCode1
Self-Supervised Learning for Endoscopic Video AnalysisCode1
MISSRec: Pre-training and Transferring Multi-modal Interest-aware Sequence Representation for RecommendationCode1
Diffusion Model as Representation LearnerCode1
FedDAT: An Approach for Foundation Model Finetuning in Multi-Modal Heterogeneous Federated LearningCode1
STEM: Unleashing the Power of Embeddings for Multi-task RecommendationCode1
PEvoLM: Protein Sequence Evolutionary Information Language ModelCode1
Exploring Transfer Learning in Medical Image Segmentation using Vision-Language ModelsCode1
Spatio-Temporal Encoding of Brain Dynamics with Surface Masked AutoencodersCode1
Multi-domain Recommendation with Embedding Disentangling and Domain AlignmentCode1
Unlocking the diagnostic potential of electrocardiograms through information transfer from cardiac magnetic resonance imagingCode1
One-stage Low-resolution Text Recognition with High-resolution Knowledge TransferCode1
Transferable Graph Structure Learning for Graph-based Traffic Forecasting Across CitiesCode1
VQGraph: Rethinking Graph Representation Space for Bridging GNNs and MLPsCode1
Pick the Best Pre-trained Model: Towards Transferability Estimation for Medical Image SegmentationCode1
Flight Contrail Segmentation via Augmented Transfer Learning with Novel SR Loss Function in Hough SpaceCode1
GEM: Boost Simple Network for Glass Surface Segmentation via Vision Foundation ModelsCode1
Transfer Learning and Bias Correction with Pre-trained Audio EmbeddingsCode1
From West to East: Who can understand the music of the others better?Code1
A Simple yet Effective Framework for Few-Shot Aspect-Based Sentiment AnalysisCode1
Class-relation Knowledge Distillation for Novel Class DiscoveryCode1
Diffusion Models Beat GANs on Image ClassificationCode1
Cumulative Spatial Knowledge Distillation for Vision TransformersCode1
Soft Prompt Tuning for Augmenting Dense Retrieval with Large Language ModelsCode1
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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