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 9926–9950 of 10307 papers

TitleStatusHype
Bayesian Joint Modelling for Object Localisation in Weakly Labelled Images—0
Using Transfer Learning for Image-Based Cassava Disease Detection—0
Knowledge Transfer for Out-of-Knowledge-Base Entities: A Graph Neural Network ApproachCode0
Consensus-Based Transfer Linear Support Vector Machines for Decentralized Multi-Task Multi-Agent Learning—0
Provable benefits of representation learning—0
Transfer Learning for Neural Semantic Parsing—0
Neural Domain Adaptation for Biomedical Question AnsweringCode0
Progressive Neural Networks for Transfer Learning in Emotion RecognitionCode0
MirBot: A collaborative object recognition system for smartphones using convolutional neural networks—0
Classifying Documents within Multiple Hierarchical Datasets using Multi-Task Learning—0
Best of Both Worlds: Transferring Knowledge from Discriminative Learning to a Generative Visual Dialog ModelCode0
Concept Transfer Learning for Adaptive Language Understanding—0
Deep-Learning Convolutional Neural Networks for scattered shrub detection with Google Earth Imagery—0
Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?—0
r-BTN: Cross-domain Face Composite and Synthesis from Limited Facial Patches—0
Krylov Subspace Recycling for Fast Iterative Least-Squares in Machine Learning—0
Transfer Learning for Speech Recognition on a BudgetCode0
Joint auto-encoders: a flexible multi-task learning framework—0
Lifelong Generative ModelingCode0
Zero-Shot Learning with Generative Latent Prototype Model—0
Deep image representations using caption generatorsCode0
Robust Data Geometric Structure Aligned Close yet Discriminative Domain Adaptation—0
Ridesourcing Car Detection by Transfer Learning—0
An Out-of-the-box Full-network Embedding for Convolutional Neural Networks—0
Effective Representations of Clinical Notes—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