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 7276–7300 of 10307 papers

TitleStatusHype
Camouflaged Variational Graph AutoEncoder against Attribute Inference Attacks for Cross-Domain Recommendation—0
The Changing Role of Entrepreneurial Universities in the Altering Innovation Policy: Opportunities Arising from the Paradigm Change in Light of the Experience of Széchenyi István University—0
The Comparison of Individual Cat Recognition Using Neural Networks—0
The Complex Negotiation Dialogue Game—0
The Costs and Benefits of Goal-Directed Attention in Deep Convolutional Neural Networks—0
The Creative Frontier of Generative AI: Managing the Novelty-Usefulness Tradeoff—0
The curious case of developmental BERTology: On sparsity, transfer learning, generalization and the brain—0
The Curse of Low Task Diversity: On the Failure of Transfer Learning to Outperform MAML and Their Empirical Equivalence—0
The Curse of Zero Task Diversity: On the Failure of Transfer Learning to Outperform MAML and their Empirical Equivalence—0
PNet -- A Deep Learning Based Photometry and Astrometry Bayesian Framework—0
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
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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