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 9451–9500 of 10307 papers

TitleStatusHype
Domain Adaptation for Sparse-Data Settings: What Do We Gain by Not Using Bert?—0
Domain Adaptation for Visual Applications: A Comprehensive Survey—0
Domain adaptation in practice: Lessons from a real-world information extraction pipeline—0
Domain Adaptation for Robot Predictive Maintenance Systems—0
Domain adaptation in small-scale and heterogeneous biological datasets—0
Domain Adaptation Meets Disentangled Representation Learning and Style Transfer—0
Domain Adaptation of low-resource Target-Domain models using well-trained ASR Conformer Models—0
Domain Adaptation of VLM for Soccer Video Understanding—0
Domain adaptation strategies for 3D reconstruction of the lumbar spine using real fluoroscopy data—0
Domain Adaptation using Silver Standard Masks for Lateral Ventricle Segmentation in FLAIR MRI—0
Domain Adaptation via Teacher-Student Learning for End-to-End Speech Recognition—0
Domain Adapted Distant Supervision for Pedagogically Motivated Relation Extraction—0
Domain adaption and physical constrains transfer learning for shale gas production—0
Domain-adaptive Fall Detection Using Deep Adversarial Training—0
Domain Adaptive Transfer Learning for Fault Diagnosis—0
Domain Adaptive Transfer Learning on Visual Attention Aware Data Augmentation for Fine-grained Visual Categorization—0
Domain Adaptive Transfer Learning with Specialist Models—0
Domain Adaptive Unfolded Graph Neural Networks—0
Domain Agnostic Few-Shot Learning For Document Intelligence—0
Domain-Agnostic Mutual Prompting for Unsupervised Domain Adaptation—0
Domain-Augmented Domain Adaptation—0
Domain-Aware Contrastive Knowledge Transfer for Multi-domain Imbalanced Data—0
Domain-decomposed image classification algorithms using linear discriminant analysis and convolutional neural networks—0
DomainForensics: Exposing Face Forgery across Domains via Bi-directional Adaptation—0
Domain Generalization: A Survey—0
Domain generalization in deep learning-based mass detection in mammography: A large-scale multi-center study—0
Domain Generalization on Medical Imaging Classification using Episodic Training with Task Augmentation—0
Domain Generalization using Ensemble Learning—0
Domain-Hierarchy Adaptation via Chain of Iterative Reasoning for Few-shot Hierarchical Text Classification—0
Domain-Independent Deception: A New Taxonomy and Linguistic Analysis—0
Domain Independent SVM for Transfer Learning in Brain Decoding—0
Domain-Invariant Feature Alignment Using Variational Inference For Partial Domain Adaptation—0
Domain-invariant Progressive Knowledge Distillation for UAV-based Object Detection—0
Domain-Invariant Projection Learning for Zero-Shot Recognition—0
Domain Mismatch Doesn't Always Prevent Cross-Lingual Transfer Learning—0
Domain Mismatch Doesn’t Always Prevent Cross-lingual Transfer Learning—0
Domain-specific loss design for unsupervised physical training: A new approach to modeling medical ML solutions—0
Domain-Specific Priors and Meta Learning for Few-Shot First-Person Action Recognition—0
Domain Specific, Semi-Supervised Transfer Learning for Medical Imaging—0
Domain-specific transfer learning in the automated scoring of tumor-stroma ratio from histopathological images of colorectal cancer—0
Domain transfer convolutional attribute embedding—0
Domain Transfer Multi-Instance Dictionary Learning—0
Don't forget, there is more than forgetting: new metrics for Continual Learning—0
Don't Just Pay Attention, PLANT It: Transfer L2R Models to Fine-tune Attention in Extreme Multi-Label Text Classification—0
Don't Parse, Insert: Multilingual Semantic Parsing with Insertion Based Decoding—0
Don't Push the Button! Exploring Data Leakage Risks in Machine Learning and Transfer Learning—0
Don’t throw away that linear head: Few-shot protein fitness prediction with generative models—0
DoQA -- Accessing Domain-Specific FAQs via Conversational QA—0
DoQA - Accessing Domain-Specific FAQs via Conversational QA—0
Dose Prediction with Deep Learning for Prostate Cancer Radiation Therapy: Model Adaptation to Different Treatment Planning Practices—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