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 3176–3200 of 10307 papers

TitleStatusHype
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
Automatic Speech Recognition for Sanskrit with Transfer Learning—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
Deep Learning-based Anomaly Detection on X-ray Images of Fuel Cell Electrodes—0
Automatic Sleep Stage Classification with Cross-modal Self-supervised Features from Deep Brain Signals—0
A Comparison of Methods for Neural Network Aggregation—0
Don't forget, there is more than forgetting: new metrics for Continual Learning—0
Adaptive Compression-Aware Split Learning and Inference for Enhanced Network Efficiency—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
Automatic segmentation of texts into units of meaning for reading assistance—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