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 8951–9000 of 10307 papers

TitleStatusHype
Easy Transfer Learning By Exploiting Intra-domain Structures—0
Lautum Regularization for Semi-supervised Transfer Learning—0
Unveiling phase transitions with machine learningCode0
Does an LSTM forget more than a CNN? An empirical study of catastrophic forgetting in NLP—0
Med3D: Transfer Learning for 3D Medical Image AnalysisCode1
Multitask Soft Option LearningCode0
Creativity Inspired Zero-Shot LearningCode0
Transfer Learning for Clinical Time Series Analysis using Deep Neural Networks—0
Local Aggregation for Unsupervised Learning of Visual EmbeddingsCode0
Cross-Subject Transfer Learning in Human Activity Recognition Systems using Generative Adversarial Networks—0
Learning to Transfer Examples for Partial Domain AdaptationCode1
A Seed-Augment-Train Framework for Universal Digit Classification—0
Understanding Unconventional Preprocessors in Deep Convolutional Neural Networks for Face Identification—0
Colorectal cancer diagnosis from histology images: A comparative study—0
A novel machine learning based framework for detection of Autism Spectrum Disorder (ASD)—0
AlphaX: eXploring Neural Architectures with Deep Neural Networks and Monte Carlo Tree SearchCode0
On evaluating CNN representations for low resource medical image classification—0
Domain Independent SVM for Transfer Learning in Brain Decoding—0
RecSys-DAN: Discriminative Adversarial Networks for Cross-Domain Recommender Systems—0
Weighted Multisource Tradaboost—0
Accelerating Deep Unsupervised Domain Adaptation with Transfer Channel Pruning—0
Training Data Independent Image Registration With GANs Using Transfer Learning And Segmentation Information—0
Enhanced Transfer Learning with ImageNet Trained Classification Layer—0
Manifold Criterion Guided Transfer Learning via Intermediate Domain GenerationCode0
Apple Leaf Disease Identification through Region-of-Interest-Aware Deep Convolutional Neural Network—0
Few-Shot Learning-Based Human Activity Recognition—0
Automatic Labeling of Data for Transfer Learning—0
Learning To Avoid Negative Transfer in Few Shot Transfer Learning—0
Automated Classification of Histopathology Images Using Transfer LearningCode0
What Synthesis is Missing: Depth Adaptation Integrated with Weak Supervision for Indoor Scene Parsing—0
A Machine Learning approach to Risk Minimisation in Electricity Markets with Coregionalized Sparse Gaussian Processes—0
Overcoming Small Minirhizotron Datasets Using Transfer LearningCode0
Deep Radiomics for Brain Tumor Detection and Classification from Multi-Sequence MRI—0
PPGnet: Deep Network for Device Independent Heart Rate Estimation from Photoplethysmogram—0
Cross Domain Knowledge Transfer for Unsupervised Vehicle Re-identification—0
Task Decomposition for Iterative Learning Model Predictive Control—0
Domain adaptation for holistic skin detection—0
Improving Prostate Cancer Detection with Breast Histopathology Images—0
To Tune or Not to Tune? Adapting Pretrained Representations to Diverse Tasks—0
Cleaning tasks knowledge transfer between heterogeneous robots: a deep learning approach—0
Paradox in Deep Neural Networks: Similar yet Different while Different yet Similar—0
Dense Classification and Implanting for Few-Shot Learning—0
Practical Semantic Parsing for Spoken Language Understanding—0
Named Entity Recognition for Electronic Health Records: A Comparison of Rule-based and Machine Learning Approaches—0
Mutual Clustering on Comparative Texts via Heterogeneous Information Networks—0
Everything old is new again: A multi-view learning approach to learning using privileged information and distillation—0
Learning Hierarchical Teaching Policies for Cooperative Agents—0
Machine learning method for single trajectory characterizationCode0
ViTOR: Learning to Rank Webpages Based on Visual Features—0
Transfer Learning Using Ensemble Neural Networks for Organic Solar Cell ScreeningCode0
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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