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 9151–9200 of 10307 papers

TitleStatusHype
Learning to Unlearn: Building Immunity to Dataset Bias in Medical Imaging Studies—0
Clinical Document Classification Using Labeled and Unlabeled Data Across Hospitals—0
Crowd Sourcing based Active Learning Approach for Parking Sign Recognition—0
A Hybrid Instance-based Transfer Learning Method—0
Transferring Knowledge across Learning ProcessesCode1
Improving Japanese semantic-role-labeling performance with transfer learning as case for limited resources of tagged corpora on aggregated language—0
Scalable Hyperparameter Transfer Learning—0
Model-Agnostic Private Learning—0
GLoMo: Unsupervised Learning of Transferable Relational Graphs—0
Policy-Conditioned Uncertainty Sets for Robust Markov Decision Processes—0
Zero-Shot Transfer with Deictic Object-Oriented Representation in Reinforcement Learning—0
Adapted Deep Embeddings: A Synthesis of Methods for k-Shot Inductive Transfer LearningCode0
Learning Curriculum Policies for Reinforcement LearningCode0
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
Hardware Conditioned Policies for Multi-Robot Transfer LearningCode0
Characterizing and Avoiding Negative Transfer—0
Differential Private Stack Generalization with an Application to Diabetes Prediction—0
CNN based dense underwater 3D scene reconstruction by transfer learning using bubble database—0
SpotTune: Transfer Learning through Adaptive Fine-tuningCode0
DNN Transfer Learning from Diversified Micro-Doppler for Motion Classification—0
Artificial Color Constancy via GoogLeNet with Angular Loss FunctionCode0
Can Synthetic Faces Undo the Damage of Dataset Bias to Face Recognition and Facial Landmark Detection?Code0
Transfer Learning Using Classification Layer Features of CNN—0
Slum Segmentation and Change Detection : A Deep Learning ApproachCode0
Larger Norm More Transferable: An Adaptive Feature Norm Approach for Unsupervised Domain AdaptationCode0
A Pretrained DenseNet Encoder for Brain Tumor Segmentation—0
Modularity in biological evolution and evolutionary computation—0
Transfer Learning with Deep CNNs for Gender Recognition and Age Estimation—0
Synonym Expansion for Large Shopping Taxonomies—0
Evaluation of deep neural networks for traffic sign detection systemsCode0
Transfer Learning for Mixed-Integer Resource Allocation Problems in Wireless Networks—0
Autonomous Extraction of a Hierarchical Structure of Tasks in Reinforcement Learning, A Sequential Associate Rule Mining Approach—0
Not just a matter of semantics: the relationship between visual similarity and semantic similarity—0
Domain Adaptive Transfer Learning with Specialist Models—0
On transfer learning using a MAC model variant—0
On Generality and Knowledge Transferability in Cross-Domain Duplicate Question Detection for Heterogeneous Community Question Answering—0
On Deep Domain Adaptation: Some Theoretical Understandings—0
Controllability, Multiplexing, and Transfer Learning in Networks using Evolutionary LearningCode0
Probabilistic Random Forest: A machine learning algorithm for noisy datasetsCode0
Performance Estimation of Synthesis Flows cross Technologies using LSTMs and Transfer Learning—0
Model-guided Multi-path Knowledge Aggregation for Aerial Saliency Prediction—0
MT-CGCNN: Integrating Crystal Graph Convolutional Neural Network with Multitask Learning for Material Property PredictionCode0
Interactive dimensionality reduction using similarity projections—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