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 5551–5575 of 10307 papers

TitleStatusHype
MetaXCR: Reinforcement-Based Meta-Transfer Learning for Cross-Lingual Commonsense Reasoning—0
Metric Embedding Autoencoders for Unsupervised Cross-Dataset Transfer Learning—0
Metric Imitation by Manifold Transfer for Efficient Vision Applications—0
Metric Learning for 3D Point Clouds Using Optimal Transport—0
MexPub: Deep Transfer Learning for Metadata Extraction from German Publications—0
MFCC-based Recurrent Neural Network for Automatic Clinical Depression Recognition and Assessment from Speech—0
MGit: A Model Versioning and Management System—0
MHTN: Modal-adversarial Hybrid Transfer Network for Cross-modal Retrieval—0
Micrometer: Micromechanics Transformer for Predicting Mechanical Responses of Heterogeneous Materials—0
Microvasculature Segmentation and Inter-capillary Area Quantification of the Deep Vascular Complex using Transfer Learning—0
MIDAS: A Dialog Act Annotation Scheme for Open Domain HumanMachine Spoken Conversations—0
MIDAS@SMM4H-2019: Identifying Adverse Drug Reactions and Personal Health Experience Mentions from Twitter—0
Migrating Knowledge between Physical Scenarios based on Artificial Neural Networks—0
MIML: Multiplex Image Machine Learning for High Precision Cell Classification via Mechanical Traits within Microfluidic Systems—0
MinConvNets: A new class of multiplication-less Neural Networks—0
MinD at SemEval-2021 Task 6: Propaganda Detection using Transfer Learning and Multimodal Fusion—0
MindForge: Empowering Embodied Agents with Theory of Mind for Lifelong Collaborative Learning—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
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
Minimax Optimal Transfer Learning for Kernel-based Nonparametric Regression—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