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 2226–2250 of 10307 papers

TitleStatusHype
Capturing Pertinent Symbolic Features for Enhanced Content-Based Misinformation DetectionCode0
MV2MAE: Multi-View Video Masked Autoencoders—0
Dynamic Prototype Adaptation with Distillation for Few-shot Point Cloud SegmentationCode0
Domain adaptation strategies for 3D reconstruction of the lumbar spine using real fluoroscopy data—0
Managing Household Waste through Transfer LearningCode0
Pre-training and Diagnosing Knowledge Base Completion ModelsCode1
A New Method for Vehicle Logo Recognition Based on Swin Transformer—0
GEM: Boost Simple Network for Glass Surface Segmentation via Segment Anything Model and Data SynthesisCode1
Exploring the Transferability of a Foundation Model for Fundus Images: Application to Hypertensive Retinopathy—0
Transfer Learning for the Prediction of Entity Modifiers in Clinical Text: Application to Opioid Use Disorder Case Detection—0
Additional Look into GAN-based Augmentation for Deep Learning COVID-19 Image Classification—0
Asymptotic Midpoint Mixup for Margin Balancing and Moderate Broadening—0
Prompt-enhanced Federated Content Representation Learning for Cross-domain RecommendationCode1
Transfer Learning With Densenet201 Architecture Model For Potato Leaf Disease Classification—0
Hierarchical Continual Reinforcement Learning via Large Language Model—0
Deep Learning Innovations in Diagnosing Diabetic Retinopathy: The Potential of Transfer Learning and the DiaCNN Model—0
StyleInject: Parameter Efficient Tuning of Text-to-Image Diffusion Models—0
A comparative study of zero-shot inference with large language models and supervised modeling in breast cancer pathology classification—0
Assessing the Portability of Parameter Matrices Trained by Parameter-Efficient Finetuning Methods—0
Don't Push the Button! Exploring Data Leakage Risks in Machine Learning and Transfer Learning—0
Towards Complementary Knowledge Distillation for Efficient Dense Image Prediction—0
Bayesian adaptive learning to latent variables via Variational Bayes and Maximum a Posteriori—0
SEDNet: Shallow Encoder-Decoder Network for Brain Tumor SegmentationCode0
Maximizing Data Efficiency for Cross-Lingual TTS Adaptation by Self-Supervised Representation Mixing and Embedding Initialization—0
Multi-Agent Based Transfer Learning for Data-Driven Air Traffic Applications—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