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 376–400 of 10307 papers

TitleStatusHype
MLNet: Mutual Learning Network with Neighborhood Invariance for Universal Domain AdaptationCode1
DTL: Disentangled Transfer Learning for Visual RecognitionCode1
MToP: A MATLAB Optimization Platform for Evolutionary MultitaskingCode1
Open-Pose 3D Zero-Shot Learning: Benchmark and ChallengesCode1
NVS-Adapter: Plug-and-Play Novel View Synthesis from a Single ImageCode1
Parameter Efficient Adaptation for Image Restoration with Heterogeneous Mixture-of-ExpertsCode1
READ: Recurrent Adapter with Partial Video-Language Alignment for Parameter-Efficient Transfer Learning in Low-Resource Video-Language ModelingCode1
Progressive Multi-Modality Learning for Inverse Protein FoldingCode1
Labrador: Exploring the Limits of Masked Language Modeling for Laboratory DataCode1
Parameter-Efficient Transfer Learning of Audio Spectrogram TransformersCode1
A Scalable and Generalizable Pathloss Map PredictionCode1
Does Vector Quantization Fail in Spatio-Temporal Forecasting? Exploring a Differentiable Sparse Soft-Vector Quantization ApproachCode1
Strong but simple: A Baseline for Domain Generalized Dense Perception by CLIP-based Transfer LearningCode1
Knowledge Transfer from Vision Foundation Models for Efficient Training of Small Task-specific ModelsCode1
Calibration-free online test-time adaptation for electroencephalography motor imagery decodingCode1
Side4Video: Spatial-Temporal Side Network for Memory-Efficient Image-to-Video Transfer LearningCode1
Unified Domain Adaptive Semantic SegmentationCode1
ConvNet vs Transformer, Supervised vs CLIP: Beyond ImageNet AccuracyCode1
Developing a Named Entity Recognition Dataset for TagalogCode1
Deep Fast Vision: A Python Library for Accelerated Deep Transfer Learning Vision PrototypingCode1
Florence-2: Advancing a Unified Representation for a Variety of Vision TasksCode1
Active Transfer Learning for Efficient Video-Specific Human Pose EstimationCode1
Transfer learning from a sparsely annotated dataset of 3D medical imagesCode1
Mini but Mighty: Finetuning ViTs with Mini AdaptersCode1
Improved Child Text-to-Speech Synthesis through Fastpitch-based Transfer LearningCode1
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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