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 10126–10150 of 10307 papers

TitleStatusHype
Adapting Models to Signal Degradation using Distillation—0
How to Transfer? Zero-Shot Object Recognition via Hierarchical Transfer of Semantic Attributes—0
Detection under Privileged Information—0
Probabilistic Reasoning via Deep Learning: Neural Association Models—0
BreakingNews: Article Annotation by Image and Text Processing—0
Recurrent Neural Network Encoder with Attention for Community Question Answering—0
Knowledge Transfer for Scene-specific Motion Prediction—0
How Transferable are Neural Networks in NLP Applications?—0
Template Adaptation for Face Verification and Identification—0
Evaluation of Deep Learning based Pose Estimation for Sign Language Recognition—0
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning—0
Knowledge Transfer with Medical Language Embeddings—0
NED: An Inter-Graph Node Metric Based On Edit DistanceCode0
Self-Transfer Learning for Fully Weakly Supervised Object Localization—0
Learning a Deep Model for Human Action Recognition from Novel Viewpoints—0
Transfer Learning Based on AdaBoost for Feature Selection from Multiple ConvNet Layer Features—0
Implicit Look-alike Modelling in Display Ads: Transfer Collaborative Filtering to CTR Estimation—0
Empirical Gaussian priors for cross-lingual transfer learning—0
Multilingual Projection for Parsing Truly Low-Resource Languages—0
Write a Classifier: Predicting Visual Classifiers from Unstructured Text—0
Cost-based Feature Transfer for Vehicle Occupant Classification—0
Domain Adaptation and Transfer Learning in StochasticNets—0
A Survey of Available Corpora for Building Data-Driven Dialogue SystemsCode0
Deep Learning-Based Image Kernel for Inductive Transfer—0
RNN Fisher Vectors for Action Recognition and Image Annotation—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