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 7576–7600 of 10307 papers

TitleStatusHype
The Bayesian Approach to Continual Learning: An Overview—0
Mind the Gap: A Generalized Approach for Cross-Modal Embedding Alignment—0
Mind the Gap Between Synthetic and Real: Utilizing Transfer Learning to Probe the Boundaries of Stable Diffusion Generated Data—0
Mind the (optimality) Gap: A Gap-Aware Learning Rate Scheduler for Adversarial Nets—0
Mind Your Language: Abuse and Offense Detection for Code-Switched Languages—0
An Adaptive Approach for Anomaly Detector Selection and Fine-Tuning in Time Series—0
An Acceleration Method Based on Deep Learning and Multilinear Feature Space—0
AMUSED: A Multi-Stream Vector Representation Method for Use in Natural Dialogue—0
A multitask transfer learning framework for the prediction of virus-human protein-protein interactions—0
MiniHack the Planet: A Sandbox for Open-Ended Reinforcement Learning Research—0
Minimally Supervised Feature Selection for Classification (Master's Thesis, University Politehnica of Bucharest)—0
Minimax And Adaptive Transfer Learning for Nonparametric Classification under Distributed Differential Privacy Constraints—0
Solvable Model for Inheriting the Regularization through Knowledge Distillation—0
Minimax Optimal Transfer Learning for Kernel-based Nonparametric Regression—0
Minimizing subject-dependent calibration for BCI with Riemannian transfer learning—0
Minimum Class Confusion based Transfer for Land Cover Segmentation in Rural and Urban Regions—0
Minimum-Norm Interpolation Under Covariate Shift—0
Min-Max Statistical Alignment for Transfer Learning—0
Minority Class Oriented Active Learning for Imbalanced Datasets—0
A Multi-Task Learning Framework for Overcoming the Catastrophic Forgetting in Automatic Speech Recognition—0
MirBot: A collaborative object recognition system for smartphones using convolutional neural networks—0
MIREncoder: Multi-modal IR-based Pretrained Embeddings for Performance Optimizations—0
Solving Euler equations with Multiple Discontinuities via Separation-Transfer Physics-Informed Neural Networks—0
Solving Large-scale Spatial Problems with Convolutional Neural Networks—0
Missing Features Reconstruction and Its Impact on Classification Accuracy—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