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 4826–4850 of 10307 papers

TitleStatusHype
Parallel Distributed Logistic Regression for Vertical Federated Learning without Third-Party Coordinator—0
Parallel Knowledge Transfer in Multi-Agent Reinforcement Learning—0
Parallel sentences mining with transfer learning in an unsupervised setting—0
Parameter-Efficient Abstractive Question Answering over Tables and over Text—0
Parameter-Efficient and Student-Friendly Knowledge Distillation—0
Parameter-efficient Dysarthric Speech Recognition Using Adapter Fusion and Householder Transformation—0
Parameter-Efficient Fine-Tuning for Medical Image Analysis: The Missed Opportunity—0
Parameter-efficient is not sufficient: Exploring Parameter, Memory, and Time Efficient Adapter Tuning for Dense Predictions—0
Parameter Efficient Mamba Tuning via Projector-targeted Diagonal-centric Linear Transformation—0
Parameter-Efficient Methods for Metastases Detection from Clinical Notes—0
Parameter-Efficient Sparse Retrievers and Rerankers using Adapters—0
Parameter-efficient transfer learning of pre-trained Transformer models for speaker verification using adapters—0
Parameter-Efficient Transfer Learning under Federated Learning for Automatic Speech Recognition—0
Parametric Variational Linear Units (PVLUs) in Deep Convolutional Networks—0
Parasitic Egg Detection and Classification in Low-cost Microscopic Images using Transfer Learning—0
ParCourE: A Parallel Corpus Explorer for a Massively Multilingual Corpus—0
Parkinson's disease diagnostics using AI and natural language knowledge transfer—0
Segmentation of Parotid Gland Tumors Using Multimodal MRI and Contrastive Learning—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
Partial Transfer Learning with Selective Adversarial Networks—0
Particle Swarm Optimisation for Evolving Deep Neural Networks for Image Classification by Evolving and Stacking Transferable Blocks—0
Commit2Vec: Learning Distributed Representations of Code Changes—0
PatchBERT: Just-in-Time, Out-of-Vocabulary Patching—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