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 9176–9200 of 10307 papers

TitleStatusHype
Feasibility of Colon Cancer Detection in Confocal Laser Microscopy Images Using Convolution Neural Networks—0
Playing Text-Adventure Games with Graph-Based Deep Reinforcement LearningCode0
Channel-wise pruning of neural networks with tapering resource constraint—0
Energy Efficient Hardware for On-Device CNN Inference via Transfer Learning—0
Crowd Sourcing based Active Learning Approach for Parking Sign Recognition—0
A Hybrid Instance-based Transfer Learning Method—0
Clinical Document Classification Using Labeled and Unlabeled Data Across Hospitals—0
Learning to Unlearn: Building Immunity to Dataset Bias in Medical Imaging Studies—0
Scalable Hyperparameter Transfer Learning—0
Learning Curriculum Policies for Reinforcement LearningCode0
Policy-Conditioned Uncertainty Sets for Robust Markov Decision Processes—0
GLoMo: Unsupervised Learning of Transferable Relational Graphs—0
Adapted Deep Embeddings: A Synthesis of Methods for k-Shot Inductive Transfer LearningCode0
Model-Agnostic Private Learning—0
Improving Japanese semantic-role-labeling performance with transfer learning as case for limited resources of tagged corpora on aggregated language—0
Zero-Shot Transfer with Deictic Object-Oriented Representation in Reinforcement Learning—0
Corresponding Projections for Orphan ScreeningCode0
Cross-database non-frontal facial expression recognition based on transductive deep transfer learning—0
On the Transferability of Representations in Neural Networks Between Datasets and Tasks—0
AI based Safety System for Employees of Manufacturing Industries in Developing Countries—0
Cross-domain Deep Feature Combination for Bird Species Classification with Audio-visual Data—0
Multi-task Learning over Graph Structures—0
ExpandNets: Linear Over-parameterization to Train Compact Convolutional Networks—0
Learning Sound Events From Webly Labeled DataCode0
Characterizing and Avoiding Negative Transfer—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