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 6276–6300 of 10307 papers

TitleStatusHype
Image-based Detection of Segment Misalignment in Multi-mirror Satellites using Transfer Learning—0
A computer vision-based model for occupancy detection using low-resolution thermal images—0
Image Based Fashion Product Recommendation with Deep Learning—0
Image-based Localization using Hourglass Networks—0
Image Classification for Snow Detection to Improve Pedestrian Safety—0
Image Credibility Analysis with Effective Domain Transferred Deep Networks—0
ImageNet performance correlates with pose estimation robustness and generalization on out-of-domain data—0
Self-supervised Visual Attribute Learning for Fashion Compatibility—0
Image Super-Resolution With Deep Variational Autoencoders—0
Image-Text-Image Knowledge Transferring for Lifelong Person Re-Identification with Hybrid Clothing States—0
Image-to-Video Re-Identification via Mutual Discriminative Knowledge Transfer—0
A Progressive Transformer for Unifying Binary Code Embedding and Knowledge Transfer—0
Self-supervised visual learning for analyzing firearms trafficking activities on the Web—0
Imagined Value Gradients: Model-Based Policy Optimization with Transferable Latent Dynamics Models—0
Imagine Flash: Accelerating Emu Diffusion Models with Backward Distillation—0
Imagine with the Teacher: Complete Shape in a Multi-View Distillation Way—0
A probabilistic constrained clustering for transfer learning and image category discovery—0
Imitating Targets from all sides: An Unsupervised Transfer Learning method for Person Re-identification—0
Imitation and Transfer Learning for LQG Control—0
Self-supervised visual learning in the low-data regime: a comparative evaluation—0
Impact of ultrasound image reconstruction method on breast lesion classification with neural transfer learning—0
Impact of visual assistance for automated audio captioning—0
Implementation of Deep Neural Networks to Classify EEG Signals using Gramian Angular Summation Field for Epilepsy Diagnosis—0
Implementation of Real-Time Lane Detection on Autonomous Mobile Robot—0
Implementing a Real-Time, YOLOv5 based Social Distancing Measuring System for Covid-19—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