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

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Showing 626–650 of 10307 papers

TitleStatusHype
Neuronal and structural differentiation in the emergence of abstract rules in hierarchically modulated spiking neural networks—0
Detection and Classification of Acute Lymphoblastic Leukemia Utilizing Deep Transfer Learning—0
Human Genome Book: Words, Sentences and ParagraphsCode0
Transfer Learning of Surrogate Models via Domain Affine Transformation Across Synthetic and Real-World Benchmarks—0
On the Transfer of Knowledge in Quantum Algorithms—0
GenTL: A General Transfer Learning Model for Building Thermal DynamicsCode0
Contrastive Representation Learning Helps Cross-institutional Knowledge Transfer: A Study in Pediatric Ventilation Management—0
NUDT4MSTAR: A Large Dataset and Benchmark Towards Remote Sensing Object Recognition in the WildCode2
Bridging The Multi-Modality Gaps of Audio, Visual and Linguistic for Speech Enhancement—0
Skin Disease Detection and Classification of Actinic Keratosis and Psoriasis Utilizing Deep Transfer Learning—0
2-Tier SimCSE: Elevating BERT for Robust Sentence Embeddings—0
WFCRL: A Multi-Agent Reinforcement Learning Benchmark for Wind Farm ControlCode1
LLM4WM: Adapting LLM for Wireless Multi-Tasking—0
EchoLM: Accelerating LLM Serving with Real-time Knowledge Distillation—0
Multimodal AI on Wound Images and Clinical Notes for Home Patient Referral—0
A novel Trunk Branch-net PINN for flow and heat transfer prediction in porous medium—0
Bidirectional Brain Image Translation using Transfer Learning from Generic Pre-trained Models—0
Heterogeneous Federated Learning Systems for Time-Series Power Consumption Prediction with Multi-Head Embedding Mechanism—0
Tackling Small Sample Survival Analysis via Transfer Learning: A Study of Colorectal Cancer PrognosisCode1
Heterogeneous Federated Learning System for Sparse Healthcare Time-Series Prediction—0
Efficient PINNs: Multi-Head Unimodular Regularization of the Solutions Space—0
Energy Consumption Reduction for UAV Trajectory Training : A Transfer Learning Approach—0
How Well Do Supervised 3D Models Transfer to Medical Imaging Tasks?Code3
Rethinking Membership Inference Attacks Against Transfer Learning—0
On the Adversarial Vulnerabilities of Transfer Learning in Remote Sensing—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