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 9001–9025 of 10307 papers

TitleStatusHype
CarbNN: A Novel Active Transfer Learning Neural Network To Build De Novo Metal Organic Frameworks (MOFs) for Carbon Capture—0
The JHU/KyotoU Speech Translation System for IWSLT 2018—0
Cardiac Segmentation using Transfer Learning under Respiratory Motion Artifacts—0
Bridge the Gap Between Visual and Linguistic Comprehension for Generalized Zero-shot Semantic Segmentation—0
Putting Question-Answering Systems into Practice: Transfer Learning for Efficient Domain Customization—0
BridgeNets: Student-Teacher Transfer Learning Based on Recursive Neural Networks and its Application to Distant Speech Recognition—0
Adaptive Transfer Learning in Deep Neural Networks: Wind Power Prediction using Knowledge Transfer from Region to Region and Between Different Task Domains—0
Bridged-GNN: Knowledge Bridge Learning for Effective Knowledge Transfer—0
Adaptive Transfer Learning for Plant Phenotyping—0
SMRS: advocating a unified reporting standard for surrogate models in the artificial intelligence era—0
Case Study of Model Adaptation: Transfer Learning and Online Learning—0
Cashew dataset generation using augmentation and RaLSGAN and a transfer learning based tinyML approach towards disease detection—0
Cataloging Accreted Stars within Gaia DR2 using Deep Learning—0
Adaptive Transfer Learning for Multi-Label Emotion Classification—0
CAT: Caution Aware Transfer in Reinforcement Learning via Distributional Risk—0
Adversarial Transfer Learning for Cross-domain Visual Recognition—0
CATrans: Context and Affinity Transformer for Few-Shot Segmentation—0
Swiss Army Knife: Synergizing Biases in Knowledge from Vision Foundation Models for Multi-Task Learning—0
QC-Automator: Deep Learning-based Automated Quality Control for Diffusion MR Images—0
QDGset: A Large Scale Grasping Dataset Generated with Quality-Diversity—0
Causal Categorization of Mental Health Posts using Transformers—0
Causal Discovery Inspired Unsupervised Domain Adaptation for Emotion-Cause Pair Extraction—0
Causal Inference based Transfer Learning with LLMs: An Efficient Framework for Industrial RUL Prediction—0
Causal Inference from Small High-dimensional Datasets—0
Causal-Invariant Cross-Domain Out-of-Distribution Recommendation—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