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 5776–5800 of 10307 papers

TitleStatusHype
Subsidiary Prototype Alignment for Universal Domain Adaptation—0
Subspace Selection to Suppress Confounding Source Domain Information in AAM Transfer Learning—0
Subsurface Depths Structure Maps Reconstruction with Generative Adversarial Networks—0
Subtask-dominated Transfer Learning for Long-tail Person Search—0
Successor Feature Neural Episodic Control—0
SuMT: A Framework of Summarization and MT—0
SUPERB-SG: Enhanced Speech processing Universal PERformance Benchmark for Semantic and Generative Capabilities—0
SUPERB-SG: Enhanced Speech processing Universal PERformance Benchmark for Semantic and Generative Capabilities—0
SuperChat: Dialogue Generation by Transfer Learning from Vision to Language using Two-dimensional Word Embedding and Pretrained ImageNet CNN Models—0
SuperLoRA: Parameter-Efficient Unified Adaptation of Multi-Layer Attention Modules—0
Super-Resolution and Image Re-projection for Iris Recognition—0
SuperTML: Two-Dimensional Word Embedding and Transfer Learning Using ImageNet Pretrained CNN Models for the Classifications on Tabular Data—0
Supervised and Contrastive Self-Supervised In-Domain Representation Learning for Dense Prediction Problems in Remote Sensing—0
Supervised and Unsupervised Transfer Learning for Question Answering—0
Supervised Contrastive Learning for Cross-lingual Transfer Learning—0
Supervised Contrastive ResNet and Transfer Learning for the In-vehicle Intrusion Detection System—0
Supervised Contrastive Vision Transformer for Breast Histopathological Image Classification—0
Supervised Denoising of Diffusion-Weighted Magnetic Resonance Images Using a Convolutional Neural Network and Transfer Learning—0
Supervised Gradual Machine Learning for Aspect Category Detection—0
Supervised Hypernymy Detection in Spanish through Order Embeddings—0
Supervised Transfer Learning at Scale for Medical Imaging—0
Supervised Transfer Learning for Product Information Question Answering—0
Supervised Transfer Learning Framework for Fault Diagnosis in Wind Turbines—0
Supervised Understanding of Word Embeddings—0
Supervising the Transfer of Reasoning Patterns in VQA—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