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 9601–9625 of 10307 papers

TitleStatusHype
Recursive Neural Programs: Variational Learning of Image Grammars and Part-Whole Hierarchies—0
Cross-Domain Few-Shot Learning with Meta Fine-Tuning—0
Cross-Domain Few-Shot Relation Extraction via Representation Learning and Domain Adaptation—0
Best Practices for Learning Domain-Specific Cross-Lingual Embeddings—0
Cross-Domain Generalization and Knowledge Transfer in Transformers Trained on Legal Data—0
Cross-Domain HAR: Few Shot Transfer Learning for Human Activity Recognition—0
Cross Domain Knowledge Transfer for Unsupervised Vehicle Re-identification—0
Cross Domain Knowledge Transfer for Person Re-identification—0
Cross-Domain Label Propagation for Domain Adaptation with Discriminative Graph Self-Learning—0
Cross-Domain Latent Modulation for Variational Transfer Learning—0
Cross-domain Microscopy Cell Counting by Disentangled Transfer Learning—0
Cross-domain Named Entity Recognition via Graph Matching—0
Recursive Tree-Structured Self-Attention for Answer Sentence Selection—0
Cross-domain Network Representations—0
Best of Both Worlds: Robust Accented Speech Recognition with Adversarial Transfer Learning—0
Cross-Domain Open-Set Machinery Fault Diagnosis Based on Adversarial Network With Multiple Auxiliary Classifiers—0
Cross-Domain Recommendation via Preference Propagation GraphNet—0
Cross-domain Recommender Systems via Multimodal Domain Adaptation—0
Cross-Domain Review Helpfulness Prediction Based on Convolutional Neural Networks with Auxiliary Domain Discriminators—0
Cross-Domain Robustness of Transformer-based Keyphrase Generation—0
Recyclable Waste Identification Using CNN Image Recognition and Gaussian Clustering—0
Best Arm Identification under Additive Transfer Bandits—0
Cross-domain Text Classification with Multiple Domains and Disparate Label Sets—0
Cross-Domain Training for Goal-Oriented Conversational Agents—0
Cross-Domain Transfer in Reinforcement Learning using Target Apprentice—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