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 4851–4875 of 10307 papers

TitleStatusHype
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
Path Planning of Cleaning Robot with Reinforcement Learning—0
Pathway to a fully data-driven geotechnics: lessons from materials informatics—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
Pay Attention to Convolution Filters: Towards Fast and Accurate Fine-Grained Transfer Learning—0
Pay Attention to Features, Transfer Learn Faster CNNs—0
PBNR: Prompt-based News Recommender System—0
PCA-Initialized Deep Neural Networks Applied To Document Image Analysis—0
PCAPVision: PCAP-Based High-Velocity and Large-Volume Network Failure Detection—0
PCNN: Environment Adaptive Model Without Finetuning—0
PCONet: A Convolutional Neural Network Architecture to Detect Polycystic Ovary Syndrome (PCOS) from Ovarian Ultrasound Images—0
PDALN: Progressive Domain Adaptation over a Pre-trained Model for Low-Resource Cross-Domain Named Entity Recognition—0
PDRL: Multi-Agent based Reinforcement Learning for Predictive Monitoring—0
Peak-Controlled Logits Poisoning Attack in Federated Distillation—0
Peer is Your Pillar: A Data-unbalanced Conditional GANs for Few-shot Image Generation—0
PEMNET: A Transfer Learning-based Modeling Approach of High-Temperature Polymer Electrolyte Membrane Electrochemical Systems—0
PEMP: Leveraging Physics Properties to Enhance Molecular Property Prediction—0
PEMT: Multi-Task Correlation Guided Mixture-of-Experts Enables Parameter-Efficient Transfer Learning—0
Percept, Chat, and then Adapt: Multimodal Knowledge Transfer of Foundation Models for Open-World Video Recognition—0
Performance Embeddings: A Similarity-based Approach to Automatic Performance Optimization—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