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 5726–5750 of 10307 papers

TitleStatusHype
Fully probabilistic design for knowledge fusion between Bayesian filters under uniform disturbances—0
FunBO: Discovering Acquisition Functions for Bayesian Optimization with FunSearch—0
Functional Annotation of Human Cognitive States using Graph Convolution Networks—0
Active Exploration in Bayesian Model-based Reinforcement Learning for Robot Manipulation—0
Functionally Regionalized Knowledge Transfer for Low-resource Drug Discovery—0
A Survey of Inductive Biases for Factorial Representation-Learning—0
Function Encoders: A Principled Approach to Transfer Learning in Hilbert Spaces—0
Functions that Emerge through End-to-End Reinforcement Learning - The Direction for Artificial General Intelligence -—0
Fundamental Computational Limits in Pursuing Invariant Causal Prediction and Invariance-Guided Regularization—0
Fundamental Limits of Transfer Learning in Binary Classifications—0
FundaQ-8: A Clinically-Inspired Scoring Framework for Automated Fundus Image Quality Assessment—0
Active Adversarial Domain Adaptation—0
Fusarium Damaged Kernels Detection Using Transfer Learning on Deep Neural Network Architecture—0
Fused Deep Neural Network based Transfer Learning in Occluded Face Classification and Person re-Identification—0
Second Thoughts are Best: Learning to Re-Align With Human Values from Text Edits—0
Secost: Sequential co-supervision for large scale weakly labeled audio event detection—0
Teaching pathology foundation models to accurately predict gene expression with parameter efficient knowledge transfer—0
G2L: A Geometric Approach for Generating Pseudo-labels that Improve Transfer Learning—0
G-Adapter: Towards Structure-Aware Parameter-Efficient Transfer Learning for Graph Transformer Networks—0
A Survey of IMU Based Cross-Modal Transfer Learning in Human Activity Recognition—0
Gain from Neighbors: Boosting Model Robustness in the Wild via Adversarial Perturbations Toward Neighboring Classes—0
Gait recognition via deep learning of the center-of-pressure trajectory—0
Galaxy Classification Using Transfer Learning and Ensemble of CNNs With Multiple Colour Spaces—0
GAMA++: Disentangled Geometric Alignment with Adaptive Contrastive Perturbation for Reliable Domain Transfer—0
Gameplay Highlights Generation—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