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 5451–5475 of 10307 papers

TitleStatusHype
Optimistic Linear Support and Successor Features as a Basis for Optimal Policy TransferCode0
Imitation Learning for Generalizable Self-driving Policy with Sim-to-real TransferCode0
Template-based Approach to Zero-shot Intent Recognition—0
KTN: Knowledge Transfer Network for Learning Multi-person 2D-3D CorrespondencesCode0
MEStereo-Du2CNN: A Novel Dual Channel CNN for Learning Robust Depth Estimates from Multi-exposure Stereo Images for HDR 3D Applications—0
An Efficient Industrial Federated Learning Framework for AIoT: A Face Recognition Application—0
A Transfer-Learning Based Ensemble Architecture for ECG Signal Classification—0
Supervised learning of random quantum circuits via scalable neural networksCode0
Building an Endangered Language Resource in the Classroom: Universal Dependencies for KakataiboCode0
Improving Localization for Semi-Supervised Object DetectionCode0
Technical Report: Combining knowledge from Transfer Learning during training and Wide ResnetsCode0
Deep representation of EEG data from Spatio-Spectral Feature Images—0
An Empirical Analysis on the Vulnerabilities of End-to-End Speech Segregation Models—0
Remote Sensing Image Classification using Transfer Learning and Attention Based Deep Neural Network—0
A Neural Network Based Method with Transfer Learning for Genetic Data Analysis—0
Terrain Classification using Transfer Learning on Hyperspectral Images: A Comparative study—0
Scalable Neural Data Server: A Data Recommender for Transfer Learning—0
Learning Multi-Task Transferable Rewards via Variational Inverse Reinforcement Learning—0
Agricultural Plantation Classification using Transfer Learning Approach based on CNN—0
Transfer Learning for Robust Low-Resource Children's Speech ASR with Transformers and Source-Filter Warping—0
Motley: Benchmarking Heterogeneity and Personalization in Federated LearningCode0
Multi-Classification of Brain Tumor Images Using Transfer Learning Based Deep Neural Network—0
A Survey on Computational Intelligence-based Transfer Learning—0
COVID-19 Detection using Transfer Learning with Convolutional Neural Network—0
TLETA: Deep Transfer Learning and Integrated Cellular Knowledge for Estimated Time of Arrival Prediction—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