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 3626–3650 of 10307 papers

TitleStatusHype
Estimating Bicycle Route Attractivity from Image Data—0
Class Relationship Embedded Learning for Source-Free Unsupervised Domain Adaptation—0
Estimating Posterior Ratio for Classification: Transfer Learning from Probabilistic Perspective—0
A Multi-Resolution Physics-Informed Recurrent Neural Network: Formulation and Application to Musculoskeletal Systems—0
Estimating Q(s,s') with Deterministic Dynamics Gradients—0
Estimating State of Charge for xEV batteries using 1D Convolutional Neural Networks and Transfer Learning—0
Estimating the influence of auxiliary tasks for multi-task learning of sequence tagging tasks—0
Ethio-Fake: Cutting-Edge Approaches to Combat Fake News in Under-Resourced Languages Using Explainable AI—0
Class-Specific Channel Attention for Few-Shot Learning—0
\'Etude de l'apprentissage par transfert de syst\`emes de traduction automatique neuronaux (Study on transfer learning in neural machine translation )—0
Class-Specific Data Augmentation: Bridging the Imbalance in Multiclass Breast Cancer Classification—0
Eva-KELLM: A New Benchmark for Evaluating Knowledge Editing of LLMs—0
Class Subset Selection for Transfer Learning using Submodularity—0
Deep CNNs for large scale species classification—0
Evaluating Cross-Lingual Transfer Learning Approaches in Multilingual Conversational Agent Models—0
Cleaning tasks knowledge transfer between heterogeneous robots: a deep learning approach—0
How to Not Measure Disentanglement—0
Fast Adaptation with Linearized Neural Networks—0
Evaluating Gaussian Grasp Maps for Generative Grasping Models—0
Fast and Scalable Expansion of Natural Language Understanding Functionality for Intelligent Agents—0
Evaluating Knowledge Transfer in Neural Network for Medical Images—0
Deep Clustering of Remote Sensing Scenes through Heterogeneous Transfer Learning—0
Automatic Audio Captioning using Attention weighted Event based Embeddings—0
Fake news detection using parallel BERT deep neural networks—0
A multi-objective perspective on jointly tuning hardware and hyperparameters—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