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 701–725 of 10307 papers

TitleStatusHype
A Simple and Effective Approach to Automatic Post-Editing with Transfer LearningCode1
Feature-based Federated Transfer Learning: Communication Efficiency, Robustness and PrivacyCode1
Bridge to Target Domain by Prototypical Contrastive Learning and Label Confusion: Re-explore Zero-Shot Learning for Slot FillingCode1
Deep Learning Enabled Semantic Communication SystemsCode1
BrainWave: A Brain Signal Foundation Model for Clinical ApplicationsCode1
Breaking the Data Barrier -- Building GUI Agents Through Task GeneralizationCode1
Bridge Correlational Neural Networks for Multilingual Multimodal Representation LearningCode1
Federated Transfer Learning for EEG Signal ClassificationCode1
FedMD: Heterogenous Federated Learning via Model DistillationCode1
Deep Learning for ECG Analysis: Benchmarks and Insights from PTB-XLCode1
Deeply Coupled Cross-Modal Prompt LearningCode1
Bridging Anaphora Resolution as Question AnsweringCode1
Bridging the User-side Knowledge Gap in Knowledge-aware Recommendations with Large Language ModelsCode1
Bridging the Source-to-target Gap for Cross-domain Person Re-Identification with Intermediate DomainsCode1
Broken Neural Scaling LawsCode1
Byakto Speech: Real-time long speech synthesis with convolutional neural network: Transfer learning from English to BanglaCode1
Bullseye Polytope: A Scalable Clean-Label Poisoning Attack with Improved TransferabilityCode1
BuildingsBench: A Large-Scale Dataset of 900K Buildings and Benchmark for Short-Term Load ForecastingCode1
DeepGaze IIE: Calibrated prediction in and out-of-domain for state-of-the-art saliency modelingCode1
An Empirical Analysis of Image-Based Learning Techniques for Malware ClassificationCode1
Calibration-free online test-time adaptation for electroencephalography motor imagery decodingCode1
CALIP: Zero-Shot Enhancement of CLIP with Parameter-free AttentionCode1
Can LLMs' Tuning Methods Work in Medical Multimodal Domain?Code1
An Empirical Investigation of Model-to-Model Distribution Shifts in Trained Convolutional FiltersCode1
Deep Transfer Learning Baselines for Sentiment Analysis in RussianCode1
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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