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 8551–8575 of 10307 papers

TitleStatusHype
Advancing Diagnostic Precision: Leveraging Machine Learning Techniques for Accurate Detection of Covid-19, Pneumonia, and Tuberculosis in Chest X-Ray Images—0
Partial Knowledge Distillation for Alleviating the Inherent Inter-Class Discrepancy in Federated Learning—0
Partially Relaxed Masks for Lightweight Knowledge Transfer without Forgetting in Continual Learning—0
Partially Supervised Unpaired Multi-Modal Learning for Label-Efficient Medical Image Segmentation—0
Advancing Automated Knowledge Transfer in Evolutionary Multitasking via Large Language Models—0
Partial Transfer Learning with Selective Adversarial Networks—0
Subspace Selection to Suppress Confounding Source Domain Information in AAM Transfer Learning—0
Particle Swarm Optimisation for Evolving Deep Neural Networks for Image Classification by Evolving and Stacking Transferable Blocks—0
Advancing 3D Point Cloud Understanding through Deep Transfer Learning: A Comprehensive Survey—0
Advances in Deep Learning for Hyperspectral Image Analysis--Addressing Challenges Arising in Practical Imaging Scenarios—0
Recent Advancements in Machine Learning For Cybercrime Prediction—0
Advances and Challenges in Meta-Learning: A Technical Review—0
Commit2Vec: Learning Distributed Representations of Code Changes—0
PatchBERT: Just-in-Time, Out-of-Vocabulary Patching—0
Patch-GAN Transfer Learning with Reconstructive Models for Cloud Removal—0
Patch-Prompt Aligned Bayesian Prompt Tuning for Vision-Language Models—0
Patent-publication pairs for the detection of knowledge transfer from research to industry: reducing ambiguities with word embeddings and references—0
The Impact of Geometric Complexity on Neural Collapse in Transfer Learning—0
Path Planning of Cleaning Robot with Reinforcement Learning—0
Pathway to a fully data-driven geotechnics: lessons from materials informatics—0
Subsurface Depths Structure Maps Reconstruction with Generative Adversarial Networks—0
Patient-Specific Domain Adaptation for Fast Optical Flow Based on Teacher-Student Knowledge Transfer—0
Patient-Specific Finetuning of Deep Learning Models for Adaptive Radiotherapy in Prostate CT—0
Pattern Transfer Learning for Reinforcement Learning in Order Dispatching—0
PaXNet: Dental Caries Detection in Panoramic X-ray using Ensemble Transfer Learning and Capsule Classifier—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