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 2026–2050 of 10307 papers

TitleStatusHype
Many-Task Federated Fine-Tuning via Unified Task Vectors—0
Generative Distribution Prediction: A Unified Approach to Multimodal Learning—0
Low Tensor-Rank Adaptation of Kolmogorov--Arnold Networks—0
Model Diffusion for Certifiable Few-shot Transfer Learning—0
Hyperparameters in Score-Based Membership Inference AttacksCode0
Target Speaker Lipreading by Audio-Visual Self-Distillation Pretraining and Speaker Adaptation—0
Contrastive Representation Distillation via Multi-Scale Feature Decoupling—0
Protecting Intellectual Property of EEG-based Neural Networks with WatermarkingCode0
Topological derivative approach for deep neural network architecture adaptation—0
SelaFD:Seamless Adaptation of Vision Transformer Fine-tuning for Radar-based Human ActivityCode0
Evaluating Standard and Dialectal Frisian ASR: Multilingual Fine-tuning and Language Identification for Improved Low-resource Performance—0
Transfer learning in Scalable Graph Neural Network for Improved Physical Simulation—0
Self-Supervised Learning for Pre-training Capsule Networks: Overcoming Medical Imaging Dataset Challenges—0
Performance Evaluation of Image Enhancement Techniques on Transfer Learning for Touchless Fingerprint Recognition—0
Provable Sample-Efficient Transfer Learning Conditional Diffusion Models via Representation Learning—0
Transfer Learning for Covert Speech Classification Using EEG Hilbert Envelope and Temporal Fine Structure—0
SWIPTNet: A Unified Deep Learning Framework for SWIPT based on GNN and Transfer Learning—0
Adaptive Prototype Knowledge Transfer for Federated Learning with Mixed Modalities and Heterogeneous Tasks—0
ICGNN: Graph Neural Network Enabled Scalable Beamforming for MISO Interference Channels—0
How does a Multilingual LM Handle Multiple Languages?—0
A Theoretical Framework for Data Efficient Multi-Source Transfer Learning Based on Cramér-Rao Bound—0
Prediction of the Most Fire-Sensitive Point in Building Structures with Differentiable Agents for Thermal Simulators—0
Transferring Graph Neural Networks for Soft Sensor Modeling using Process Topologies—0
TopoCL: Topological Contrastive Learning for Time Series—0
Complying with the EU AI Act: Innovations in Explainable and User-Centric Hand Gesture Recognition—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