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 8201–8225 of 10307 papers

TitleStatusHype
Transfer Representation Learning with TSK Fuzzy System—0
Transfer Reward Learning for Policy Gradient-Based Text Generation—0
Transferring a molecular foundation model for polymer property predictions—0
Transferring Autonomous Driving Knowledge on Simulated and Real Intersections—0
Transferring Chemical and Energetic Knowledge Between Molecular Systems with Machine Learning—0
Transferring CLIP's Knowledge into Zero-Shot Point Cloud Semantic Segmentation—0
Unsupervised Transfer Learning for Anomaly Detection: Application to Complementary Operating Condition Transfer—0
Transferring Core Knowledge via Learngenes—0
Transferring Domain-Agnostic Knowledge in Video Question Answering—0
Transferring Dual Stochastic Graph Convolutional Network for Facial Micro-expression Recognition—0
Transferring Expectations in Model-based Reinforcement Learning—0
Transferring Graph Neural Networks for Soft Sensor Modeling using Process Topologies—0
Transferring Knowledge Fragments for Learning Distance Metric from A Heterogeneous Domain—0
Transferring Knowledge from Discourse to Arguments: A Case Study with Scientific Abstracts—0
Transferring Knowledge from High-Quality to Low-Quality MRI for Adult Glioma Diagnosis—0
Transferring Knowledge from Large Foundation Models to Small Downstream Models—0
Transferring Knowledge from Vision to Language: How to Achieve it and how to Measure it?—0
Transferring model structure in Bayesian transfer learning for Gaussian process regression—0
Transferring Monolingual Model to Low-Resource Language: The Case of Tigrinya—0
Transferring SLU Models in Novel Domains—0
Transferring speech-generic and depression-specific knowledge for Alzheimer's disease detection—0
Transferring User Interests Across Websites with Unstructured Text for Cold-Start Recommendation—0
Transfer Risk Map: Mitigating Pixel-level Negative Transfer in Medical Segmentation—0
Transfer RL via the Undo Maps Formalism—0
Transfer Value Iteration Networks—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