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 1901–1925 of 10307 papers

TitleStatusHype
Cramnet: Layer-wise Deep Neural Network Compression with Knowledge Transfer from a Teacher Network—0
CoLI-Machine Learning Approaches for Code-mixed Language Identification at the Word Level in Kannada-English Texts—0
Collaborative Adversarial Learning for RelationalLearning on Multiple Bipartite Graphs—0
Are We Ready for Out-of-Distribution Detection in Digital Pathology?—0
Collaborative Group Learning—0
Collaborative Knowledge Fusion: A Novel Approach for Multi-task Recommender Systems via LLMs—0
Learning with Shared Representations: Statistical Rates and Efficient Algorithms—0
Collaborative Memory: Multi-User Memory Sharing in LLM Agents with Dynamic Access Control—0
Collaborative Pressure Ulcer Prevention: An Automated Skin Damage and Pressure Ulcer Assessment Tool for Nursing Professionals, Patients, Family Members and Carers—0
Collaborative Recommendation with Auxiliary Data: A Transfer Learning View—0
ChemVise: Maximizing Out-of-Distribution Chemical Detection with the Novel Application of Zero-Shot Learning—0
Collaborative Teacher-Student Learning via Multiple Knowledge Transfer—0
Accelerating Dependency Graph Learning from Heterogeneous Categorical Event Streams via Knowledge Transfer—0
Char-RNN for Word Stress Detection in East Slavic Languages—0
Collective Knowledge Graph Completion with Mutual Knowledge Distillation—0
Collective Wisdom: Improving Low-resource Neural Machine Translation using Adaptive Knowledge Distillation—0
Collusion Detection with Graph Neural Networks—0
Colorectal cancer diagnosis from histology images: A comparative study—0
A Physics-driven GraphSAGE Method for Physical Process Simulations Described by Partial Differential Equations—0
CReaM: Condensed Real-time Models for Depth Prediction using Convolutional Neural Networks—0
Characterizing and Understanding the Generalization Error of Transfer Learning with Gibbs Algorithm—0
Combination of Domain Knowledge and Deep Learning for Sentiment Analysis of Short and Informal Messages on Social Media—0
Combination of multiple neural networks using transfer learning and extensive geometric data augmentation for assessing cellularity scores in histopathology images—0
Transfer-Recursive-Ensemble Learning for Multi-Day COVID-19 Prediction in India using Recurrent Neural Networks—0
Characterizing and Avoiding Negative Transfer—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