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 5926–5950 of 10307 papers

TitleStatusHype
The Deep Radial Basis Function Data Descriptor (D-RBFDD) Network: A One-Class Neural Network for Anomaly Detection—0
The design and implementation of Language Learning Chatbot with XAI using Ontology and Transfer Learning—0
The Details Matter: Preventing Class Collapse in Supervised Contrastive Learning—0
The Devil is in the Tails: Fine-grained Classification in the Wild—0
Dynamics and Reachability of Learning Tasks—0
The effectiveness of unsupervised subword modeling with autoregressive and cross-lingual phone-aware networks—0
The Effect of Q-function Reuse on the Total Regret of Tabular, Model-Free, Reinforcement Learning—0
The effect of variable labels on deep learning models trained to predict breast density—0
The Effects of Input Type and Pronunciation Dictionary Usage in Transfer Learning for Low-Resource Text-to-Speech—0
The elements of flexibility for task-performing systems—0
The Empirical Impact of Forgetting and Transfer in Continual Visual Odometry—0
The Evolution and Future Perspectives of Artificial Intelligence Generated Content—0
The Evolution of Reinforcement Learning in Quantitative Finance: A Survey—0
The Fast and Accurate Approach to Detection and Segmentation of Melanoma Skin Cancer using Fine-tuned Yolov3 and SegNet Based on Deep Transfer Learning—0
The First Multilingual Model For The Detection of Suicide Texts—0
The Frechet Distance of training and test distribution predicts the generalization gap—0
Bayesian Active Learning in the Presence of Nuisance Parameters—0
The Geometry of Self-supervised Learning Models and its Impact on Transfer Learning—0
The Global Banking Standards QA Dataset (GBS-QA)—0
The HIT-SCIR System for End-to-End Parsing of Universal Dependencies—0
The impact of data set similarity and diversity on transfer learning success in time series forecasting—0
The Impact of Geometric Complexity on Neural Collapse in Transfer Learning—0
The impact of near domain transfer on biomedical named entity recognition—0
Transient Non-Stationarity and Generalisation in Deep Reinforcement Learning—0
The Impact of Preprocessing on Deep Representations for Iris Recognition on Unconstrained Environments—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