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 1851–1875 of 10307 papers

TitleStatusHype
Reliable Model Watermarking: Defending Against Theft without Compromising on Evasion—0
Transfer Learning for Molecular Property Predictions from Small Data SetsCode0
MultiConfederated Learning: Inclusive Non-IID Data handling with Decentralized Federated Learning—0
MergeNet: Knowledge Migration across Heterogeneous Models, Tasks, and Modalities—0
Federated Transfer Learning with Task Personalization for Condition Monitoring in Ultrasonic Metal Welding—0
KATO: Knowledge Alignment and Transfer for Transistor Sizing of Different Design and Technology—0
Explainable AI for Fair Sepsis Mortality Predictive Model—0
Cross-Modal Adapter: Parameter-Efficient Transfer Learning Approach for Vision-Language Models—0
sEMG-based Fine-grained Gesture Recognition via Improved LightGBM Model—0
Understanding Optimal Feature Transfer via a Fine-Grained Bias-Variance Analysis—0
Control Theoretic Approach to Fine-Tuning and Transfer Learning—0
Neuron Specialization: Leveraging intrinsic task modularity for multilingual machine translation—0
Explainable Lung Disease Classification from Chest X-Ray Images Utilizing Deep Learning and XAI—0
Supervised Contrastive Vision Transformer for Breast Histopathological Image Classification—0
Feature Corrective Transfer Learning: End-to-End Solutions to Object Detection in Non-Ideal Visual Conditions—0
GenFighter: A Generative and Evolutive Textual Attack Removal—0
Lighter, Better, Faster Multi-Source Domain Adaptation with Gaussian Mixture Models and Optimal TransportCode0
Privacy-Enhanced Training-as-a-Service for On-Device Intelligence: Concept, Architectural Scheme, and Open Problems—0
Tao: Re-Thinking DL-based Microarchitecture Simulation—0
Self-Supervised Learning Featuring Small-Scale Image Dataset for Treatable Retinal Diseases Classification—0
High-Resolution Detection of Earth Structural Heterogeneities from Seismic Amplitudes using Convolutional Neural Networks with Attention layers—0
Multiple-Input Fourier Neural Operator (MIFNO) for source-dependent 3D elastodynamicsCode1
Conditional Prototype Rectification Prompt LearningCode0
CREST: Cross-modal Resonance through Evidential Deep Learning for Enhanced Zero-Shot LearningCode0
Evaluating Fast Adaptability of Neural Networks for Brain-Computer InterfaceCode0
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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