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 6351–6375 of 10307 papers

TitleStatusHype
A Computational Model of Inclusive Pedagogy: From Understanding to Application—0
A Pretrained DenseNet Encoder for Brain Tumor Segmentation—0
A Preliminary Study on Environmental Sound Classification Leveraging Large-Scale Pretrained Model and Semi-Supervised Learning—0
Improving Cross-Lingual Transfer Learning for End-to-End Speech Recognition with Speech Translation—0
Improving Customer Service Chatbots with Attention-based Transfer Learning—0
Improving data sharing and knowledge transfer via the Neuroelectrophysiology Analysis Ontology (NEAO)—0
A predictive physics-aware hybrid reduced order model for reacting flows—0
Improving Device Directedness Classification of Utterances with Semantic Lexical Features—0
Semantic decoupled representation learning for remote sensing image change detection—0
Semantic-Discriminative Mixup for Generalizable Sensor-based Cross-domain Activity Recognition—0
Improving ECG-based COVID-19 diagnosis and mortality predictions using pre-pandemic medical records at population-scale—0
Improving Event Coreference Resolution by Learning Argument Compatibility from Unlabeled Data—0
Improving Feature Extraction from Histopathological Images Through A Fine-tuning ImageNet Model—0
A Practical Guide to Fine-tuning Language Models with Limited Data—0
Improving filling level classification with adversarial training—0
Improving Generalizability of Extracting Social Determinants of Health Using Large Language Models through Prompt-tuning—0
Improving generalization in reinforcement learning through forked agents—0
Semantic-diversity transfer network for generalized zero-shot learning via inner disagreement based OOD detector—0
Improving Generalization of Transfer Learning Across Domains Using Spatio-Temporal Features in Autonomous Driving—0
Improving Hyperparameter Optimization by Planning Ahead—0
Improving Image Classification Robustness through Selective CNN-Filters Fine-Tuning—0
A Practical Approach towards Causality Mining in Clinical Text using Active Transfer Learning—0
Improving Japanese semantic-role-labeling performance with transfer learning as case for limited resources of tagged corpora on aggregated language—0
Improving Knowledge Distillation in Transfer Learning with Layer-wise Learning Rates—0
Improving Language and Modality Transfer in Translation by Character-level Modeling—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