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 4426–4450 of 10307 papers

TitleStatusHype
CrAFT: Compression-Aware Fine-Tuning for Efficient Visual Task Adaptation—0
Model-Contrastive Federated Domain Adaptation—0
Cross-domain Augmentation Networks for Click-Through Rate Prediction—0
Approximation by non-symmetric networks for cross-domain learning—0
Towards a Simple Framework of Skill Transfer Learning for Robotic Ultrasound-guidance ProceduresCode0
Towards Effective Collaborative Learning in Long-Tailed Recognition—0
Knowledge Transfer from Teachers to Learners in Growing-Batch Reinforcement Learning—0
Online Gesture Recognition using Transformer and Natural Language Processing—0
Emulation Learning for Neuromimetic Systems—0
An Imitation Learning Based Algorithm Enabling Priori Knowledge Transfer in Modern Electricity Markets for Bayesian Nash Equilibrium Estimation—0
DocLangID: Improving Few-Shot Training to Identify the Language of Historical DocumentsCode0
Transfer and Active Learning for Dissonance Detection: Addressing the Rare-Class ChallengeCode0
Improving Cancer Hallmark Classification with BERT-based Deep Learning Approach—0
Parameter-Efficient Cross-lingual Transfer of Vision and Language Models via Translation-based AlignmentCode0
Cross-Institutional Transfer Learning for Educational Models: Implications for Model Performance, Fairness, and EquityCode0
EvoluNet: Advancing Dynamic Non-IID Transfer Learning on GraphsCode0
Redundancy and Concept Analysis for Code-trained Language Models—0
Detect, Distill and Update: Detect, Distill and Update: Learned DB Systems Facing Out of Distribution DataCode0
Deception Detection with Feature-Augmentation by soft Domain Transfer—0
CLIP-S^4: Language-Guided Self-Supervised Semantic Segmentation—0
Procedural Content Generation via Knowledge Transformation (PCG-KT)—0
Towards Unbiased Training in Federated Open-world Semi-supervised Learning—0
Transfer of knowledge among instruments in automatic music transcription—0
Optimized Machine Learning for CHD Detection using 3D CNN-based Segmentation, Transfer Learning and Adagrad Optimization—0
The ART of Transfer Learning: An Adaptive and Robust Pipeline—0
Show:102550
← PrevPage 178 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