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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 3401–3425 of 10307 papers

TitleStatusHype
Deep Neural Network Models Trained With A Fixed Random Classifier Transfer Better Across Domains—0
Automated Testing of Spatially-Dependent Environmental Hypotheses through Active Transfer Learning—0
Emotion Classification in Low and Moderate Resource Languages—0
Investigating Continual Pretraining in Large Language Models: Insights and Implications—0
Transfer Learning Bayesian Optimization to Design Competitor DNA Molecules for Use in Diagnostic AssaysCode0
Beyond the Known: Investigating LLMs Performance on Out-of-Domain Intent Detection—0
Intensive Care as One Big Sequence Modeling ProblemCode0
DTCM: Deep Transformer Capsule Mutual Distillation for Multivariate Time Series Classification—0
Few-Shot Learning for Annotation-Efficient Nucleus Instance Segmentation—0
GenAINet: Enabling Wireless Collective Intelligence via Knowledge Transfer and Reasoning—0
Enhancing Continuous Domain Adaptation with Multi-Path Transfer Curriculum—0
Adversarial-Robust Transfer Learning for Medical Imaging via Domain Assimilation—0
Exploring the Power of Pure Attention Mechanisms in Blind Room Parameter Estimation—0
StochCA: A Novel Approach for Exploiting Pretrained Models with Cross-AttentionCode0
Emotion Classification in Short English Texts using Deep Learning Techniques—0
Task Specific Pretraining with Noisy Labels for Remote Sensing Image Segmentation—0
MMW-Carry: Enhancing Carry Object Detection through Millimeter-Wave Radar-Camera Fusion—0
Artificial Bee Colony optimization of Deep Convolutional Neural Networks in the context of Biomedical Imaging—0
Substrate Prediction for RiPP Biosynthetic Enzymes via Masked Language Modeling and Transfer LearningCode0
Which Model to Transfer? A Survey on Transferability Estimation—0
PEMT: Multi-Task Correlation Guided Mixture-of-Experts Enables Parameter-Efficient Transfer Learning—0
CLCE: An Approach to Refining Cross-Entropy and Contrastive Learning for Optimized Learning Fusion—0
Smoothness Adaptive Hypothesis Transfer Learning—0
Practical Insights into Knowledge Distillation for Pre-Trained Models—0
Global Safe Sequential Learning via Efficient Knowledge TransferCode0
Show:102550
← PrevPage 137 of 413Next →
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