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 3251–3275 of 10307 papers

TitleStatusHype
Dual Relation Mining Network for Zero-Shot Learning—0
Dual Scale-aware Adaptive Masked Knowledge Distillation for Object Detection—0
Automatic Recognition of the Supraspinatus Tendinopathy from Ultrasound Images using Convolutional Neural Networks—0
Dual-stream contrastive predictive network with joint handcrafted feature view for SAR ship classification—0
Analysis and Adaptation of YOLOv4 for Object Detection in Aerial Images—0
Deep Learning Approach Combining Lightweight CNN Architecture with Transfer Learning: An Automatic Approach for the Detection and Recognition of Bangladeshi Banknotes—0
Deep Learning Applied to Chest X-Rays: Exploiting and Preventing Shortcuts—0
Automatic Recognition of Food Ingestion Environment from the AIM-2 Wearable Sensor—0
A Data Augmented Approach to Transfer Learning for Covid-19 Detection—0
DVS: Blood cancer detection using novel CNN-based ensemble approach—0
Enhancing Cocoa Pod Disease Classification via Transfer Learning and Ensemble Methods: Toward Robust Predictive Modeling—0
Dyn-Adapter: Towards Disentangled Representation for Efficient Visual Recognition—0
Enhancing Continuous Domain Adaptation with Multi-Path Transfer Curriculum—0
Automatic Recognition of Coal and Gangue based on Convolution Neural Network—0
Dynamically Composing Domain-Data Selection with Clean-Data Selection by ``Co-Curricular Learning'' for Neural Machine Translation—0
Dynamically enhanced static handwriting representation for Parkinson's disease detection—0
Dynamically Updating Event Representations for Temporal Relation Classification with Multi-category Learning—0
Dynamically writing coupled memories using a reinforcement learning agent, meeting physical bounds—0
Anomaly Detection Using Computer Vision: A Comparative Analysis of Class Distinction and Performance Metrics—0
Dynamic Corrective Self-Distillation for Better Fine-Tuning of Pretrained Models—0
Deep Learning and Machine Learning, Advancing Big Data Analytics and Management: Tensorflow Pretrained Models—0
A Comparison of LSTM and BERT for Small Corpus—0
Dynamic Ensemble Reasoning for LLM Experts—0
Dynamic Flows on Curved Space Generated by Labeled Data—0
Deep learning and high harmonic generation—0
Show:102550
← PrevPage 131 of 413Next →
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