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 7551–7575 of 10307 papers

TitleStatusHype
ACNLP at SemEval-2020 Task 6: A Supervised Approach for Definition Extraction—0
Soft Representation Learning for Sparse Transfer—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
Software Vulnerability Prediction Knowledge Transferring Between Programming Languages—0
MGit: A Model Versioning and Management System—0
MHTN: Modal-adversarial Hybrid Transfer Network for Cross-modal Retrieval—0
An adaptive transfer learning perspective on classification in non-stationary environments—0
Theater Aid System for the Visually Impaired Through Transfer Learning of Spatio-Temporal Graph Convolution Networks—0
Micrometer: Micromechanics Transformer for Predicting Mechanical Responses of Heterogeneous Materials—0
SOLA-GCL: Subgraph-Oriented Learnable Augmentation Method for Graph Contrastive Learning—0
Microvasculature Segmentation and Inter-capillary Area Quantification of the Deep Vascular Complex using Transfer Learning—0
SolNet: Open-source deep learning models for photovoltaic power forecasting across the globe—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
An adaptive human-in-the-loop approach to emission detection of Additive Manufacturing processes and active learning with computer vision—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
A closer look at network resolution for efficient network design—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
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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