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 426–450 of 10307 papers

TitleStatusHype
Asymmetric Decision-Making in Online Knowledge Distillation:Unifying Consensus and Divergence—0
HFedCKD: Toward Robust Heterogeneous Federated Learning via Data-free Knowledge Distillation and Two-way Contrast—0
Transfer Learning for LQR Control—0
MoFE: Mixture of Frozen Experts Architecture—0
MetaXCR: Reinforcement-Based Meta-Transfer Learning for Cross-Lingual Commonsense Reasoning—0
Adversarial Robustness of Discriminative Self-Supervised Learning in Vision—0
NeuroADDA: Active Discriminative Domain Adaptation in Connectomic—0
Data-Free Black-Box Federated Learning via Zeroth-Order Gradient Estimation—0
Text-Speech Language Models with Improved Cross-Modal Transfer by Aligning Abstraction Levels—0
Statistical Deficiency for Task Inclusion Estimation—0
Partially Supervised Unpaired Multi-Modal Learning for Label-Efficient Medical Image Segmentation—0
CACTUS: An Open Dataset and Framework for Automated Cardiac Assessment and Classification of Ultrasound Images Using Deep Transfer Learning—0
Spatial Distillation based Distribution Alignment (SDDA) for Cross-Headset EEG ClassificationCode1
opXRD: Open Experimental Powder X-ray Diffraction Database—0
Implicit Cross-Lingual Rewarding for Efficient Multilingual Preference AlignmentCode0
TinyR1-32B-Preview: Boosting Accuracy with Branch-Merge Distillation—0
Sarcasm Detection as a Catalyst: Improving Stance Detection with Cross-Target Capabilities—0
PGAD: Prototype-Guided Adaptive Distillation for Multi-Modal Learning in AD Diagnosis—0
Intermediate-Task Transfer Learning: Leveraging Sarcasm Detection for Stance Detection—0
MA-LoT: Multi-Agent Lean-based Long Chain-of-Thought Reasoning enhances Formal Theorem ProvingCode1
A Quantum Neural Network Transfer-Learning Model for Forecasting Problems with Continuous and Discrete Variables—0
A Phylogenetic Approach to Genomic Language ModelingCode3
Remote Sensing Image Classification Using Convolutional Neural Network (CNN) and Transfer Learning Techniques—0
X2CT-CLIP: Enable Multi-Abnormality Detection in Computed Tomography from Chest Radiography via Tri-Modal Contrastive Learning—0
Teaching AI to Handle Exceptions: Supervised Fine-Tuning with Human-Aligned Judgment—0
Show:102550
← PrevPage 18 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