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 4701–4725 of 10307 papers

TitleStatusHype
IDPG: An Instance-Dependent Prompt Generation Method—0
IDPG: An Instance-Dependent Prompt Generation Method—0
"I'd rather just go to bed": Understanding Indirect Answers—0
``I'd rather just go to bed'': Understanding Indirect Answers—0
iFuzzyTL: Interpretable Fuzzy Transfer Learning for SSVEP BCI System—0
IGOR: Image-GOal Representations are the Atomic Control Units for Foundation Models in Embodied AI—0
iLab-20M: A Large-Scale Controlled Object Dataset to Investigate Deep Learning—0
Image Analytics for Legal Document Review: A Transfer Learning Approach—0
Image augmentation improves few-shot classification performance in plant disease recognition—0
Image-based Detection of Segment Misalignment in Multi-mirror Satellites using Transfer Learning—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
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
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
Imitating Targets from all sides: An Unsupervised Transfer Learning method for Person Re-identification—0
Imitation and Transfer Learning for LQG Control—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
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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