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 4226–4250 of 10307 papers

TitleStatusHype
Differentially Private Video Activity Recognition—0
Semi-supervised Multimodal Representation Learning through a Global WorkspaceCode0
Transfer Learning across Several Centuries: Machine and Historian Integrated Method to Decipher Royal Secretary's Diary—0
Transfer: Cross Modality Knowledge Transfer using Adversarial Networks -- A Study on Gesture Recognition—0
Deep Transfer Learning for Intelligent Vehicle Perception: a Survey—0
A Collaborative Transfer Learning Framework for Cross-domain Recommendation—0
Feature Adversarial Distillation for Point Cloud Classification—0
Semi-supervised Object Detection: A Survey on Recent Research and Progress—0
A Web-based Mpox Skin Lesion Detection System Using State-of-the-art Deep Learning Models Considering Racial DiversityCode0
Cross-domain Recommender Systems via Multimodal Domain Adaptation—0
Variance-Covariance Regularization Improves Representation Learning—0
Master-ASR: Achieving Multilingual Scalability and Low-Resource Adaptation in ASR with Modular Learning—0
TaCA: Upgrading Your Visual Foundation Model with Task-agnostic Compatible AdapterCode0
Natural Language Processing in Electronic Health Records in Relation to Healthcare Decision-making: A Systematic Review—0
Transferable Curricula through Difficulty Conditioned Generators—0
Wildfire Detection Via Transfer Learning: A Survey—0
Strategies in Transfer Learning for Low-Resource Speech Synthesis: Phone Mapping, Features Input, and Source Language Selection—0
Benchmark data to study the influence of pre-training on explanation performance in MR image classification—0
Introspective Action Advising for Interpretable Transfer Learning—0
Meta-Analysis of Transfer Learning for Segmentation of Brain Lesions—0
MSVD-Indonesian: A Benchmark for Multimodal Video-Text Tasks in IndonesianCode0
EEG Decoding for Datasets with Heterogenous Electrode Configurations using Transfer Learning Graph Neural Networks—0
DynaQuant: Compressing Deep Learning Training Checkpoints via Dynamic Quantization—0
Knowledge Distillation via Token-level Relationship Graph—0
MuDPT: Multi-modal Deep-symphysis Prompt Tuning for Large Pre-trained Vision-Language ModelsCode0
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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