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 7751–7775 of 10307 papers

TitleStatusHype
Structure Mapping for Transferability of Causal ModelsCode0
DWMD: Dimensional Weighted Orderwise Moment Discrepancy for Domain-specific Hidden Representation Matching—0
2nd Place Solution to ECCV 2020 VIPriors Object Detection Challenge—0
Domain2Vec: Domain Embedding for Unsupervised Domain AdaptationCode0
ImageNet performance correlates with pose estimation robustness and generalization on out-of-domain data—0
CovidCare: Transferring Knowledge from Existing EMR to Emerging Epidemic for Interpretable Prognosis—0
Multi-Stage Influence Function—0
Transfer Learning without Knowing: Reprogramming Black-box Machine Learning Models with Scarce Data and Limited Resources—0
Vehicle Detection of Multi-source Remote Sensing Data Using Active Fine-tuning Network—0
Transfer Deep Reinforcement Learning-enabled Energy Management Strategy for Hybrid Tracked Vehicle—0
Collaborative Adversarial Learning for RelationalLearning on Multiple Bipartite Graphs—0
Advances in Deep Learning for Hyperspectral Image Analysis--Addressing Challenges Arising in Practical Imaging Scenarios—0
Faster Uncertainty Quantification for Inverse Problems with Conditional Normalizing Flows—0
Two-Level Adversarial Visual-Semantic Coupling for Generalized Zero-shot Learning—0
Visualizing Transfer Learning—0
Vehicle Trajectory Prediction by Transfer Learning of Semi-Supervised Models—0
Lifelong Learning using Eigentasks: Task Separation, Skill Acquisition, and Selective Transfer—0
Representation Transfer by Optimal Transport—0
FADACS: A Few-shot Adversarial Domain Adaptation Architecture for Context-Aware Parking Availability Sensing—0
Data-driven geophysics: from dictionary learning to deep learning—0
Transfer Learning for Activity Recognition in Mobile HealthCode0
Transfer learning extensions for the probabilistic classification vector machine—0
Nodule2vec: a 3D Deep Learning System for Pulmonary Nodule Retrieval Using Semantic Representation—0
Enhanced Behavioral Cloning Based self-driving Car Using Transfer Learning—0
Pre-trained Word Embeddings for Goal-conditional Transfer Learning in Reinforcement Learning—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