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 8176–8200 of 10307 papers

TitleStatusHype
Star-Graph Multimodal Matching Component Analysis for Data Fusion and Transfer Learning—0
Aggregated Multi-output Gaussian Processes with Knowledge Transfer Across Domains—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
Age Range Estimation using MTCNN and VGG-Face Model—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
STAR: Noisy Semi-Supervised Transfer Learning for Visual Classification—0
StARS DCM: A Sleep Stage-Decoding Forehead EEG Patch for Real-time Modulation of Sleep Physiology—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
State Classification with CNN—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
State-of-the-art and gaps for deep learning on limited training data in remote sensing—0
Agent-Specific Deontic Modality Detection in Legal Language—0
Novel Fundus Image Preprocessing for Retcam Images to Improve Deep Learning Classification of Retinopathy of Prematurity—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