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 5376–5400 of 10307 papers

TitleStatusHype
S^4-Tuning: A Simple Cross-lingual Sub-network Tuning Method—0
S^4-Tuning: A Simple Cross-lingual Sub-network Tuning Method—0
S-Adapter: Generalizing Vision Transformer for Face Anti-Spoofing with Statistical Tokens—0
SPD-CFL: Stepwise Parameter Dropout for Efficient Continual Federated Learning—0
SafeNet: The Unreasonable Effectiveness of Ensembles in Private Collaborative Learning—0
SAFFRON: tranSfer leArning For Food-disease RelatiOn extractioN—0
SA-GDA: Spectral Augmentation for Graph Domain Adaptation—0
SAJA at TRAC 2020 Shared Task: Transfer Learning for Aggressive Identification with XGBoost—0
Salienteye: Maximizing Engagement While Maintaining Artistic Style on Instagram Using Deep Neural Networks—0
Learning to Adapt SAM for Segmenting Cross-domain Point Clouds—0
SAM-Lightening: A Lightweight Segment Anything Model with Dilated Flash Attention to Achieve 30 times Acceleration—0
Sample-based Regularization: A Transfer Learning Strategy Toward Better Generalization—0
Sample-Efficient Bayesian Transfer Learning for Online Machine Parameter Optimization—0
Sample-Efficient Deep Learning for COVID-19 Diagnosis Based on CT Scans—0
Sample-Efficient Reinforcement Learning through Transfer and Architectural Priors—0
Sampling to Distill: Knowledge Transfer from Open-World Data—0
Sarcasm Detection as a Catalyst: Improving Stance Detection with Cross-Target Capabilities—0
SASSL: Enhancing Self-Supervised Learning via Neural Style Transfer—0
Say What? Collaborative Pop Lyric Generation Using Multitask Transfer Learning—0
SB-MTL: Score-based Meta Transfer-Learning for Cross-Domain Few-Shot Learning—0
SB_NITK at MEDIQA 2021: Leveraging Transfer Learning for Question Summarization in Medical Domain—0
Scalable and Interpretable Contextual Bandits: A Literature Review and Retail Offer Prototype—0
Scalable and reliable deep transfer learning for intelligent fault detection via multi-scale neural processes embedded with knowledge—0
Scalable Cross-Lingual Transfer of Neural Sentence Embeddings—0
Scalable Differential Privacy With Sparse Network Finetuning—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