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 5601–5650 of 10307 papers

TitleStatusHype
MobileCLIP: Fast Image-Text Models through Multi-Modal Reinforced Training—0
MobileTL: On-device Transfer Learning with Inverted Residual Blocks—0
Mobile Traffic Prediction at the Edge Through Distributed and Deep Transfer Learning—0
MoCo-Pretraining Improves Representations and Transferability of Chest X-ray Models—0
Modality-bridge Transfer Learning for Medical Image Classification—0
ModalPrompt:Dual-Modality Guided Prompt for Continual Learning of Large Multimodal Models—0
Model Adaptation for Personalized Opinion Analysis—0
Model Adaption Object Detection System for Robot—0
Model-Agnostic Meta-Learning for EEG Motor Imagery Decoding in Brain-Computer-Interfacing—0
Model-Agnostic Private Learning—0
Model-Agnostic Round-Optimal Federated Learning via Knowledge Transfer—0
Model-based adaptation for sample efficient transfer in reinforcement learning control of parameter-varying systems—0
Deep Model-Based Reinforcement Learning for High-Dimensional Problems, a Survey—0
Model-based Large Language Model Customization as Service—0
Model-based Reinforcement Learning: A Survey—0
Task Decomposition for Iterative Learning Model Predictive Control—0
Model Bias in NLP -- Application to Hate Speech Classification using transfer learning techniques—0
Model-Contrastive Federated Domain Adaptation—0
Model Diffusion for Certifiable Few-shot Transfer Learning—0
Model Distillation with Knowledge Transfer from Face Classification to Alignment and Verification—0
Model-Driven Beamforming Neural Networks—0
Model ensemble instead of prompt fusion: a sample-specific knowledge transfer method for few-shot prompt tuning—0
Model Evaluation for Domain Identification of Unknown Classes in Open-World Recognition: A Proposal—0
Model-Free Generative Replay for Lifelong Reinforcement Learning: Application to Starcraft-2—0
Modeling & Evaluating the Performance of Convolutional Neural Networks for Classifying Steel Surface Defects—0
Modeling Information Flow Through Deep Neural Networks—0
Unleashing the Power of Shared Label Structures for Human Activity Recognition—0
Modeling Social Norms Evolution for Personalized Sentiment Classification—0
Model Inversion Attack against Transfer Learning: Inverting a Model without Accessing It—0
Model Inversion Robustness: Can Transfer Learning Help?—0
Modelling Domain Relationships for Transfer Learning on Retrieval-based Question Answering Systems in E-commerce—0
Modelling the Neuroanatomical Progression of Alzheimer's Disease and Posterior Cortical Atrophy—0
Model Parallel Training and Transfer Learning for Convolutional Neural Networks by Domain Decomposition—0
Model-Robust and Adaptive-Optimal Transfer Learning for Tackling Concept Shifts in Nonparametric Regression—0
Out of Thin Air: Exploring Data-Free Adversarial Robustness Distillation—0
Model Selection, Adaptation, and Combination for Transfer Learning in Wind and Photovoltaic Power Forecasts—0
Model Selection for Cross-Lingual Transfer using a Learned Scoring Function—0
Model Transport: Towards Scalable Transfer Learning on Manifolds—0
Model Tuning or Prompt Tuning? A Study of Large Language Models for Clinical Concept and Relation Extraction—0
Modular Approach to Machine Reading Comprehension: Mixture of Task-Aware Experts—0
Modular Deep Learning—0
Modularity in biological evolution and evolutionary computation—0
Modularized data-driven approximation of the Koopman operator and generator—0
Modularized Transfer Learning with Multiple Knowledge Graphs for Zero-shot Commonsense Reasoning—0
Modularized Transfer Learning with Multiple Knowledge Graphs for Zero-shot Commonsense Reasoning—0
Modular network for high accuracy object detection—0
Modular Transfer Learning with Transition Mismatch Compensation for Excessive Disturbance Rejection—0
MoE-CT: A Novel Approach For Large Language Models Training With Resistance To Catastrophic Forgetting—0
MO-EMT-NAS: Multi-Objective Continuous Transfer of Architectural Knowledge Between Tasks from Different Datasets—0
MoFE: Mixture of Frozen Experts Architecture—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