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 5326–5350 of 10307 papers

TitleStatusHype
Risk Stratification of Lung Nodules Using 3D CNN-Based Multi-task Learning—0
RNN Fisher Vectors for Action Recognition and Image Annotation—0
RoadScan: A Novel and Robust Transfer Learning Framework for Autonomous Pothole Detection in Roads—0
RoBERT -- A Romanian BERT Model—0
robo-gym -- An Open Source Toolkit for Distributed Deep Reinforcement Learning on Real and Simulated Robots—0
Robotic and Generative Adversarial Attacks in Offline Writer-independent Signature Verification—0
Robotic self-representation improves manipulation skills and transfer learning—0
Robot Policy Transfer with Online Demonstrations: An Active Reinforcement Learning Approach—0
Robots Learn Increasingly Complex Tasks with Intrinsic Motivation and Automatic Curriculum Learning—0
Robust Activity Recognition for Adaptive Worker-Robot Interaction using Transfer Learning—0
Robust Adaptation of Foundation Models with Black-Box Visual Prompting—0
Robust agents learn causal world models—0
Robust and Explainable Fine-Grained Visual Classification with Transfer Learning: A Dual-Carriageway Framework—0
Robust and Explainable Framework to Address Data Scarcity in Diagnostic Imaging—0
Robust Audio-Visual Instance Discrimination—0
Robust Authorship Verification with Transfer Learning—0
Robust Computer Vision in an Ever-Changing World: A Survey of Techniques for Tackling Distribution Shifts—0
Robust Data Geometric Structure Aligned Close yet Discriminative Domain Adaptation—0
Robust Deep Sensing Through Transfer Learning in Cognitive Radio—0
Robust Few-shot Transfer Learning for Knowledge Base Question Answering with Unanswerable Questions—0
Robust Generalization of Quadratic Neural Networks via Function Identification—0
Robustifying Sequential Neural Processes—0
Robust Importance Sampling for Error Estimation in the Context of Optimal Bayesian Transfer Learning—0
Robust Indoor Localization in Dynamic Environments: A Multi-source Unsupervised Domain Adaptation Framework—0
Robust Instruction-Following in a Situated Agent via Transfer-Learning from Text—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