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 5801–5825 of 10307 papers

TitleStatusHype
Generalization of feature embeddings transferred from different video anomaly detection domains—0
Generalization Performance of Transfer Learning: Overparameterized and Underparameterized Regimes—0
Team Cogitat at NeurIPS 2021: Benchmarks for EEG Transfer Learning Competition—0
Seed Phenotyping on Neural Networks using Domain Randomization and Transfer Learning—0
Generalized and Transferable Patient Language Representation for Phenotyping with Limited Data—0
Action Recognition using Transfer Learning and Majority Voting for CSGO—0
Generalized Cross-domain Multi-label Few-shot Learning for Chest X-rays—0
A study on the plasticity of neural networks—0
Generalized Domain Adaptation with Covariate and Label Shift CO-ALignment—0
Class-imbalanced Domain Adaptation: An Empirical Odyssey—0
A Study on Robustness to Perturbations for Representations of Environmental Sound—0
Action Recognition for American Sign Language—0
Generalized Graphon Process: Convergence of Graph Frequencies in Stretched Cut Distance—0
Generalized Online Transfer Learning for Climate Control in Residential Buildings—0
A Study on Representation Transfer for Few-Shot Learning—0
Generalized User Representations for Transfer Learning—0
Generalized Zero and Few-Shot Transfer for Facial Forgery Detection—0
Generalizing Deep Whole Brain Segmentation for Pediatric and Post-Contrast MRI with Augmented Transfer Learning—0
Transferring Fairness using Multi-Task Learning with Limited Demographic Information—0
Team Innovators at SemEval-2022 for Task 8: Multi-Task Training with Hyperpartisan and Semantic Relation for Multi-Lingual News Article Similarity—0
Seg4Reg+: Consistency Learning between Spine Segmentation and Cobb Angle Regression—0
The OCR Quest for Generalization: Learning to recognize low-resource alphabets with model editing—0
Generalizing Vision-Language Models to Novel Domains: A Comprehensive Survey—0
Automatic Recognition of the General-Purpose Communicative Functions defined by the ISO 24617-2 Standard for Dialog Act Annotation—0
SegBook: A Simple Baseline and Cookbook for Volumetric Medical Image Segmentation—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