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 9851–9875 of 10307 papers

TitleStatusHype
Cross-lingual Character-Level Neural Morphological Tagging—0
Transfer Learning and Sentence Level Features for Named Entity Recognition on Tweets—0
Transfer Learning across Low-Resource, Related Languages for Neural Machine Translation—0
Multi-task Dictionary Learning based Convolutional Neural Network for Computer aided Diagnosis with Longitudinal Images—0
Cross-lingual, Character-Level Neural Morphological Tagging—0
Subspace Selection to Suppress Confounding Source Domain Information in AAM Transfer Learning—0
Folding membrane proteins by deep transfer learning—0
k-Nearest Neighbor Augmented Neural Networks for Text Classification—0
Accelerating Dependency Graph Learning from Heterogeneous Categorical Event Streams via Knowledge Transfer—0
Exploiting Convolution Filter Patterns for Transfer Learning—0
Human experts vs. machines in taxa recognition—0
Stacked transfer learning for tropical cyclone intensity prediction—0
Causally Regularized Learning with Agnostic Data Selection Bias—0
Representation Learning by Learning to CountCode0
Revisiting knowledge transfer for training object class detectors—0
A Deep Q-Network for the Beer Game: A Deep Reinforcement Learning algorithm to Solve Inventory Optimization Problems—0
Learning to Transfer—0
Belief Tree Search for Active Object Recognition—0
What matters in a transferable neural network model for relation classification in the biomedical domain?—0
Modality-bridge Transfer Learning for Medical Image Classification—0
Probabilistic Neural Network with Complex Exponential Activation Functions in Image Recognition using Deep Learning Framework—0
MHTN: Modal-adversarial Hybrid Transfer Network for Cross-modal Retrieval—0
ISS-MULT: Intelligent Sample Selection for Multi-Task Learning in Question Answering—0
Fully Convolutional Networks for Diabetic Foot Ulcer Segmentation—0
Sensing Urban Land-Use Patterns By Integrating Google Tensorflow And Scene-Classification Models—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