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 1676–1700 of 10307 papers

TitleStatusHype
Empowering Source-Free Domain Adaptation with MLLM-driven Curriculum LearningCode0
Gradually Vanishing Gap in Prototypical Network for Unsupervised Domain Adaptation—0
Cost-Sensitive Multi-Fidelity Bayesian Optimization with Transfer of Learning Curve Extrapolation—0
MultiADE: A Multi-domain Benchmark for Adverse Drug Event Extraction—0
An adaptive transfer learning perspective on classification in non-stationary environments—0
FlashST: A Simple and Universal Prompt-Tuning Framework for Traffic PredictionCode2
XTrack: Multimodal Training Boosts RGB-X Video Object TrackersCode2
Cross-Context Backdoor Attacks against Graph Prompt LearningCode0
A Survey of Latent Factor Models in Recommender Systems—0
Can We Trust LLMs? Mitigate Overconfidence Bias in LLMs through Knowledge Transfer—0
Harnessing the Power of Vicinity-Informed Analysis for Classification under Covariate Shift—0
Enhancing Accuracy in Generative Models via Knowledge Transfer—0
Dual-State Personalized Knowledge Tracing with Emotional Incorporation—0
Transfer Learning for Diffusion Models—0
Daily Physical Activity Monitoring -- Adaptive Learning from Multi-source Motion Sensor Data—0
From Macro to Micro: Boosting micro-expression recognition via pre-training on macro-expression videos—0
Image-Text-Image Knowledge Transferring for Lifelong Person Re-Identification with Hybrid Clothing States—0
Transfer Learning Under High-Dimensional Graph Convolutional Regression Model for Node Classification—0
Acceleration of Grokking in Learning Arithmetic Operations via Kolmogorov-Arnold Representation—0
Mixture of Latent Experts Using Tensor Products—0
Noisy Data Meets Privacy: Training Local Models with Post-Processed Remote Queries—0
LoGAH: Predicting 774-Million-Parameter Transformers using Graph HyperNetworks with 1/100 ParametersCode1
Generation of synthetic data using breast cancer dataset and classification with resnet18—0
A transfer learning framework for weak-to-strong generalization—0
Uncovering cognitive taskonomy through transfer learning in masked autoencoder-based fMRI reconstruction—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