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 4601–4625 of 10307 papers

TitleStatusHype
NLP-CIC-WFU at SocialDisNER: Disease Mention Extraction in Spanish Tweets Using Transfer Learning and Search by Propagation—0
NLP in the DH pipeline: Transfer-learning to a Chronolect—0
NodeTrans: A Graph Transfer Learning Approach for Traffic Prediction—0
Nodule2vec: a 3D Deep Learning System for Pulmonary Nodule Retrieval Using Semantic Representation—0
NoEsis: Differentially Private Knowledge Transfer in Modular LLM Adaptation—0
Noise-Aware Training of Layout-Aware Language Models—0
Noise-Response Analysis of Deep Neural Networks Quantifies Robustness and Fingerprints Structural Malware—0
Non-Causal to Causal SSL-Supported Transfer Learning: Towards a High-Performance Low-Latency Speech Vocoder—0
Non-Contrastive Self-supervised Learning for Utterance-Level Information Extraction from Speech—0
Nondestructive chicken egg fertility detection using CNN-transfer learning algorithms—0
Nonhuman Primate Brain Tissue Segmentation Using a Transfer Learning Approach—0
Non-native children speech recognition through transfer learning—0
Non-stationary and Sparsely-correlated Multi-output Gaussian Process with Spike-and-Slab Prior—0
Non-transferable Pruning—0
Non-Uniform Class-Wise Coreset Selection: Characterizing Category Difficulty for Data-Efficient Transfer Learning—0
KonVid-150k: A Dataset for No-Reference Video Quality Assessment of Videos in-the-Wild—0
Not 3D Re-ID: a Simple Single Stream 2D Convolution for Robust Video Re-identification—0
Not again! Data Leakage in Digital Pathology—0
Not All Areas Are Equal: Transfer Learning for Semantic Segmentation via Hierarchical Region Selection—0
\#NotAWhore! A Computational Linguistic Perspective of Rape Culture and Victimization on Social Media—0
Not just a matter of semantics: the relationship between visual similarity and semantic similarity—0
Not-so-supervised: a survey of semi-supervised, multi-instance, and transfer learning in medical image analysis—0
Novel Artistic Scene-Centric Datasets for Effective Transfer Learning in Fragrant Spaces—0
Novel Fundus Image Preprocessing for Retcam Images to Improve Deep Learning Classification of Retinopathy of Prematurity—0
Novel Machine Learning Approach for Predicting Poverty using Temperature and Remote Sensing Data in Ethiopia—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