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 2376–2400 of 10307 papers

TitleStatusHype
Effective Analog ICs Floorplanning with Relational Graph Neural Networks and Reinforcement Learning—0
MindForge: Empowering Embodied Agents with Theory of Mind for Lifelong Collaborative Learning—0
Domain Adaptive Unfolded Graph Neural Networks—0
Classification of Geographical Land Structure Using Convolution Neural Network and Transfer Learning—0
Adversarial Multi-Agent Reinforcement Learning for Proactive False Data Injection Detection—0
MLDGG: Meta-Learning for Domain Generalization on Graphs—0
Multivariate and Online Transfer Learning with Uncertainty Quantification—0
Compression of Higher Order Ambisonics with Multichannel RVQGAN—0
In-Situ Melt Pool Characterization via Thermal Imaging for Defect Detection in Directed Energy Deposition Using Vision Transformers—0
Transmission Line Defect Detection Based on UAV Patrol Images and Vision-language Pretraining—0
Efficient Transfer Learning for Video-language Foundation ModelsCode0
Adaptive Learning of Design Strategies over Non-Hierarchical Multi-Fidelity Models via Policy Alignment—0
DiHuR: Diffusion-Guided Generalizable Human Reconstruction—0
Everything is a Video: Unifying Modalities through Next-Frame Prediction—0
Federated Domain Generalization via Prompt Learning and AggregationCode0
FedCL-Ensemble Learning: A Framework of Federated Continual Learning with Ensemble Transfer Learning Enhanced for Alzheimer's MRI Classifications while Preserving Privacy—0
Unlocking Transfer Learning for Open-World Few-Shot Recognition—0
Towards Sample-Efficiency and Generalization of Transfer and Inverse Reinforcement Learning: A Comprehensive Literature Review—0
Causal Time-Series Synchronization for Multi-Dimensional Forecasting—0
Domain Adaptation-based Edge Computing for Cross-Conditions Fault Diagnosis—0
mmSpyVR: Exploiting mmWave Radar for Penetrating Obstacles to Uncover Privacy Vulnerability of Virtual RealityCode0
A Centralized-Distributed Transfer Model for Cross-Domain Recommendation Based on Multi-Source Heterogeneous Transfer Learning—0
Harnessing multiple LLMs for Information Retrieval: A case study on Deep Learning methodologies in Biodiversity publicationsCode0
A Practical Guide to Fine-tuning Language Models with Limited Data—0
Edge Caching Optimization with PPO and Transfer Learning for Dynamic Environments—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