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 3001–3025 of 10307 papers

TitleStatusHype
FedMEKT: Distillation-based Embedding Knowledge Transfer for Multimodal Federated Learning—0
Sparse annotation strategies for segmentation of short axis cardiac MRI—0
End-to-End Deep Transfer Learning for Calibration-free Motor Imagery Brain Computer Interfaces—0
NCART: Neural Classification and Regression Tree for Tabular Data—0
An X3D Neural Network Analysis for Runner's Performance Assessment in a Wild Sporting Environment—0
Identifying Misinformation on YouTube through Transcript Contextual Analysis with Transformer ModelsCode0
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
Pick the Best Pre-trained Model: Towards Transferability Estimation for Medical Image SegmentationCode1
Transfer Learning and Bias Correction with Pre-trained Audio EmbeddingsCode1
Pluvio: Assembly Clone Search for Out-of-domain Architectures and Libraries through Transfer Learning and Conditional Variational Information Bottleneck—0
Eye Disease Classification Using Deep Learning Techniques—0
Novel Batch Active Learning Approach and Its Application to Synthetic Aperture Radar DatasetsCode0
Determination of the critical points for systems of directed percolation class using machine learning—0
Gradient Sparsification For Masked Fine-Tuning of Transformers—0
Revisiting invariances and introducing priors in Gromov-Wasserstein distancesCode0
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
Detecting Throat Cancer from Speech Signals using Machine Learning: A Scoping Literature Review—0
Alioth: A Machine Learning Based Interference-Aware Performance Monitor for Multi-Tenancy Applications in Public CloudCode0
Class-relation Knowledge Distillation for Novel Class DiscoveryCode1
Evaluate Fine-tuning Strategies for Fetal Head Ultrasound Image Segmentation with U-NetCode0
FISTNet: FusIon of STyle-path generative Networks for Facial Style Transfer—0
Distilling Knowledge from Text-to-Image Generative Models Improves Visio-Linguistic Reasoning in CLIP—0
A Survey on Open-Vocabulary Detection and Segmentation: Past, Present, and FutureCode2
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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