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 1026–1050 of 10307 papers

TitleStatusHype
Classification of Large-Scale High-Resolution SAR Images with Deep Transfer LearningCode1
A Closer Look at Few-shot Classification AgainCode1
CausalWorld: A Robotic Manipulation Benchmark for Causal Structure and Transfer LearningCode1
CARLANE: A Lane Detection Benchmark for Unsupervised Domain Adaptation from Simulation to multiple Real-World DomainsCode1
AdaptGuard: Defending Against Universal Attacks for Model AdaptationCode1
CEM500K – A large-scale heterogeneous unlabeled cellular electron microscopy image dataset for deep learningCode1
Can AI help in screening Viral and COVID-19 pneumonia?Code1
Can LLMs' Tuning Methods Work in Medical Multimodal Domain?Code1
Calibration-free online test-time adaptation for electroencephalography motor imagery decodingCode1
DeepGaze IIE: Calibrated prediction in and out-of-domain for state-of-the-art saliency modelingCode1
CALIP: Zero-Shot Enhancement of CLIP with Parameter-free AttentionCode1
Can LLM Watermarks Robustly Prevent Unauthorized Knowledge Distillation?Code1
CFA: Coupled-hypersphere-based Feature Adaptation for Target-Oriented Anomaly LocalizationCode1
Class-relation Knowledge Distillation for Novel Class DiscoveryCode1
Bridging the User-side Knowledge Gap in Knowledge-aware Recommendations with Large Language ModelsCode1
AdapterHub Playground: Simple and Flexible Few-Shot Learning with AdaptersCode1
Broken Neural Scaling LawsCode1
Bridge to Target Domain by Prototypical Contrastive Learning and Label Confusion: Re-explore Zero-Shot Learning for Slot FillingCode1
Bridging Anaphora Resolution as Question AnsweringCode1
A Survey: Deep Learning for Hyperspectral Image Classification with Few Labeled SamplesCode1
Bridge Correlational Neural Networks for Multilingual Multimodal Representation LearningCode1
Bridging the Source-to-target Gap for Cross-domain Person Re-Identification with Intermediate DomainsCode1
BuildingsBench: A Large-Scale Dataset of 900K Buildings and Benchmark for Short-Term Load ForecastingCode1
Boosting Weakly Supervised Object Detection via Learning Bounding Box AdjustersCode1
AgileGAN: stylizing portraits by inversion-consistent transfer learningCode1
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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