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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 10276–10300 of 10307 papers

TitleStatusHype
Transfer Learning for Constituency-Based Grammars—0
Controlling the Precision-Recall Tradeoff in Differential Dependency Network Analysis—0
Bayesian Discovery of Multiple Bayesian Networks via Transfer Learning—0
Learning Fair RepresentationsCode0
Identifying Intention Posts in Discussion Forums—0
Designing Category-Level Attributes for Discriminative Visual Recognition—0
From N to N+1: Multiclass Transfer Incremental Learning—0
Zipfian corruptions for robust POS tagging—0
Transfer Learning for Content-Based Recommender Systems using Tree Matching—0
Inverse Density as an Inverse Problem: The Fredholm Equation Approach—0
The Impact of Selectional Preference Agreement on Semantic Relational Similarity—0
Exploiting Social Tags for Cross-Domain Collaborative Filtering—0
Transfer Learning Using Logistic Regression in Credit Scoring—0
Transferring Expectations in Model-based Reinforcement Learning—0
Semi-supervised Learning of Naive Bayes Classifier with feature constraints—0
Semi-supervised Chinese Word Segmentation for CLP2012—0
A P300 BCI for the Masses: Prior Information Enables Instant Unsupervised Spelling—0
Sparse coding for multitask and transfer learning—0
The Arcade Learning Environment: An Evaluation Platform for General AgentsCode0
Active Learning with Transfer Learning—0
Large-Scale Feature Learning With Spike-and-Slab Sparse Coding—0
On Causal and Anticausal LearningCode0
Phrase-Based Approach for Adaptive Tokenization—0
Analyzing Urdu Social Media for Sentiments using Transfer Learning with Controlled Translations—0
PAC-Bayesian Policy Evaluation for Reinforcement Learning—0
Show:102550
← PrevPage 412 of 413Next →
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