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 4076–4100 of 10307 papers

TitleStatusHype
Findings of the Covid-19 MLIA Machine Translation Task—0
Finding the Most Transferable Tasks for Brain Image Segmentation—0
Active Learning with Transfer Learning—0
Estimating Q(s,s') with Deterministic Dynamics Gradients—0
Classroom-Inspired Multi-Mentor Distillation with Adaptive Learning Strategies—0
Estimating Posterior Ratio for Classification: Transfer Learning from Probabilistic Perspective—0
Class Relationship Embedded Learning for Source-Free Unsupervised Domain Adaptation—0
A Prior Knowledge Based Tumor and Tumoral Subregion Segmentation Tool for Pediatric Brain Tumors—0
Fine-Grained Object Recognition and Zero-Shot Learning in Remote Sensing Imagery—0
Estimating Bicycle Route Attractivity from Image Data—0
A Primer on Pretrained Multilingual Language Models—0
Adversary Helps: Gradient-based Device-Free Domain-Independent Gesture Recognition—0
Fine-Grained Vehicle Classification with Unsupervised Parts Co-occurrence Learning—0
ESCORT: Ethereum Smart COntRacTs Vulnerability Detection using Deep Neural Network and Transfer Learning—0
Fine-to-coarse Knowledge Transfer For Low-Res Image Classification—0
FINETUNA: Fine-tuning Accelerated Molecular Simulations—0
Fine-tuned Generative Adversarial Network-based Model for Medical Image Super-Resolution—0
Classifying Judgements using Transfer Learning—0
EriBERTa: A Bilingual Pre-Trained Language Model for Clinical Natural Language Processing—0
Classifying Documents within Multiple Hierarchical Datasets using Multi-Task Learning—0
Equitable Skin Disease Prediction Using Transfer Learning and Domain Adaptation—0
Classification of Skin Disease Using Transfer Learning in Convolutional Neural Networks—0
EPUTION at SemEval-2018 Task 2: Emoji Prediction with User Adaption—0
Classification of Skin Cancer Images using Convolutional Neural Networks—0
A Pretrained DenseNet Encoder for Brain Tumor Segmentation—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