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 5626–5650 of 10307 papers

TitleStatusHype
Five lessons from building a deep neural network recommender—0
A Survey on Self-supervised Pre-training for Sequential Transfer Learning in Neural Networks—0
A Survey on Multilingual Large Language Models: Corpora, Alignment, and Bias—0
SCD-Net: Spatiotemporal Clues Disentanglement Network for Self-supervised Skeleton-based Action Recognition—0
A Survey on Model-based, Heuristic, and Machine Learning Optimization Approaches in RIS-aided Wireless Networks—0
Scene-adaptive and Region-aware Multi-modal Prompt for Open Vocabulary Object Detection—0
Using LLMs to Establish Implicit User Sentiment of Software Desirability—0
Flexible deep transfer learning by separate feature embeddings and manifold alignment—0
FLEXIBLE: Forecasting Cellular Traffic by Leveraging Explicit Inductive Graph-Based Learning—0
Scenes-Objects-Actions: A Multi-Task, Multi-Label Video Dataset—0
Flexible Transfer Learning under Support and Model Shift—0
FlexPose: Pose Distribution Adaptation with Limited Guidance—0
A Survey on Machine Learning Techniques for Auto Labeling of Video, Audio, and Text Data—0
A Survey on Heterogeneous Federated Learning—0
A Survey on Incorporating Domain Knowledge into Deep Learning for Medical Image Analysis—0
A survey on domain adaptation theory: learning bounds and theoretical guarantees—0
Fluorescent Neuronal Cells v2: Multi-Task, Multi-Format Annotations for Deep Learning in Microscopy—0
Active Learning of Ordinal Embeddings: A User Study on Football Data—0
A Survey on Deep Transfer Learning—0
Focal Cortical Dysplasia Type II Detection Using Cross Modality Transfer Learning and Grad-CAM in 3D-CNNs for MRI Analysis—0
Teacher-student curriculum learning for reinforcement learning—0
FOI DSS at SemEval-2018 Task 1: Combining LSTM States, Embeddings, and Lexical Features for Affect Analysis—0
Schrödinger's Tree -- On Syntax and Neural Language Models—0
Folding membrane proteins by deep transfer learning—0
FoMo: A Foundation Model for Mobile Traffic Forecasting with Diffusion Model—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