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 8126–8150 of 10307 papers

TitleStatusHype
A Helping Hand: Transfer Learning for Deep Sentiment Analysis—0
neuralRank: Searching and ranking ANN-based model repositories—0
Neural Relation Extraction via Inner-Sentence Noise Reduction and Transfer Learning—0
Neural Sign Language Translation by Learning Tokenization—0
Neural Skill Transfer from Supervised Language Tasks to Reading Comprehension—0
Stacked Denoising Autoencoders and Transfer Learning for Immunogold Particles Detection and Recognition—0
Stacked Semantic-Guided Network for Zero-Shot Sketch-Based Image Retrieval—0
Neural Supervised Domain Adaptation by Augmenting Pre-trained Models with Random Units—0
Stacked transfer learning for tropical cyclone intensity prediction—0
A Hands-on Comparison of DNNs for Dialog Separation Using Transfer Learning from Music Source Separation—0
Neural Transfer Learning for Cry-based Diagnosis of Perinatal Asphyxia—0
A Growing Long-term Episodic & Semantic Memory—0
Introduction to Neural Transfer Learning with Transformers for Social Science Text Analysis—0
Agricultural Plantation Classification using Transfer Learning Approach based on CNN—0
NeuroADDA: Active Discriminative Domain Adaptation in Connectomic—0
Neuroevolutionary Transfer Learning of Deep Recurrent Neural Networks through Network-Aware Adaptation—0
Neuron Specialization: Leveraging intrinsic task modularity for multilingual machine translation—0
Neuropsychiatric Disease Classification Using Functional Connectomics -- Results of the Connectomics in NeuroImaging Transfer Learning Challenge—0
Neurosymbolic Artificial Intelligence for Robust Network Intrusion Detection: From Scratch to Transfer Learning—0
Stacking for Transfer Learning—0
Stain Normalized Breast Histopathology Image Recognition using Convolutional Neural Networks for Cancer Detection—0
Neutral TTS Female Voice Corpus in Brazilian Portuguese—0
Agreement Tracking for Multi-Issue Negotiation Dialogues—0
New Directions for Language Resource Development and Distribution—0
Accelerating evolutionary exploration through language model-based transfer 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