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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 3326–3350 of 10307 papers

TitleStatusHype
Deep Reinforcement Learning to Maximize Arterial Usage during Extreme Congestion—0
Deep Ensembling for Perceptual Image Quality Assessment—0
A Whisper transformer for audio captioning trained with synthetic captions and transfer learningCode1
Self-Supervised Pretraining on Paired Sequences of fMRI Data for Transfer Learning to Brain Decoding Tasks—0
CLIP-VG: Self-paced Curriculum Adapting of CLIP for Visual GroundingCode1
Meta-models for transfer learning in source localisation—0
Predictive Models from Quantum Computer Benchmarks—0
Towards Automated COVID-19 Presence and Severity Classification—0
An Ensemble Approach for Automated Theorem Proving Based on Efficient Name Invariant Graph Neural RepresentationsCode1
Parameter-Efficient Fine-Tuning for Medical Image Analysis: The Missed Opportunity—0
Learning to Learn Unlearned Feature for Brain Tumor Segmentation—0
Surface EMG-Based Inter-Session/Inter-Subject Gesture Recognition by Leveraging Lightweight All-ConvNet and Transfer Learning—0
How to Train Your CheXDragon: Training Chest X-Ray Models for Transfer to Novel Tasks and Healthcare Systems—0
Cross-Language Transfer Learning using Visual Information for Automatic Sign Gesture Recognition—0
Intelligent multicast routing method based on multi-agent deep reinforcement learning in SDWN—0
ML-Based Teaching Systems: A Conceptual Framework—0
To transfer or not transfer: Unified transferability metric and analysis—0
Prompt Learning to Mitigate Catastrophic Forgetting in Cross-lingual Transfer for Open-domain Dialogue GenerationCode0
Spider GAN: Leveraging Friendly Neighbors to Accelerate GAN TrainingCode0
Learning representations that are closed-form Monge mapping optimal with application to domain adaptationCode0
Meta-hallucinator: Towards Few-Shot Cross-Modality Cardiac Image Segmentation—0
A Deep Learning-based Compression and Classification Technique for Whole Slide Histopathology Images—0
Serial Contrastive Knowledge Distillation for Continual Few-shot Relation ExtractionCode1
Medical supervised masked autoencoders: Crafting a better masking strategy and efficient fine-tuning schedule for medical image classificationCode0
Adapt and Align to Improve Zero-Shot Sketch-Based Image Retrieval—0
Show:102550
← PrevPage 134 of 413Next →
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