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 726–750 of 10307 papers

TitleStatusHype
Gain from Neighbors: Boosting Model Robustness in the Wild via Adversarial Perturbations Toward Neighboring Classes—0
Mixture of Submodules for Domain Adaptive Person Search—0
Meta-Learning Hyperparameters for Parameter Efficient Fine-Tuning—0
TADFormer: Task-Adaptive Dynamic TransFormer for Efficient Multi-Task Learning—0
Navigating Nuance: In Quest for Political TruthCode0
Beyond Model Scale Limits: End-Edge-Cloud Federated Learning with Self-Rectified Knowledge Agglomeration—0
Advanced Lung Nodule Segmentation and Classification for Early Detection of Lung Cancer using SAM and Transfer Learning—0
Spatio-Temporal Multi-Subgraph GCN for 3D Human Motion Prediction—0
Addressing Challenges in Data Quality and Model Generalization for Malaria Detection—0
Leveraging AI for Automatic Classification of PCOS Using Ultrasound ImagingCode0
Human-AI Teaming Using Large Language Models: Boosting Brain-Computer Interfacing (BCI) and Brain Research—0
Class-based Subset Selection for Transfer Learning under Extreme Label Shift—0
Improving Location-based Thermal Emission Side-Channel Analysis Using Iterative Transfer Learning—0
Depression and Anxiety Prediction Using Deep Language Models and Transfer Learning—0
Investigating layer-selective transfer learning of QAOA parameters for Max-Cut problem—0
Attention Is All You Need For Mixture-of-Depths Routing—0
Sample Correlation for Fingerprinting Deep Face RecognitionCode0
Optical Character Recognition using Convolutional Neural Networks for Ashokan Brahmi Inscriptions—0
On Adversarial Robustness of Language Models in Transfer Learning—0
Enhancing Entertainment Translation for Indian Languages using Adaptive Context, Style and LLMs—0
LEARNER: A Transfer Learning Method for Low-Rank Matrix EstimationCode0
LLM-Virus: Evolutionary Jailbreak Attack on Large Language ModelsCode0
SimLTD: Simple Supervised and Semi-Supervised Long-Tailed Object DetectionCode1
Enhancing Transfer Learning for Medical Image Classification with SMOTE: A Comparative Study—0
VisTabNet: Adapting Vision Transformers for Tabular DataCode0
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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