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 3676–3700 of 10307 papers

TitleStatusHype
Accumulating Knowledge for Lifelong Online Learning—0
Code-Mixed Text to Speech Synthesis under Low-Resource Constraints—0
A Review of Automated Diagnosis of COVID-19 Based on Scanning Images—0
A Review of Artificial Intelligence Technologies for Early Prediction of Alzheimer's Disease—0
Co-Creative Learning via Metropolis-Hastings Interaction between Humans and AI—0
CoCoNet: A Collaborative Convolutional Network—0
CoCo DistillNet: a Cross-layer Correlation Distillation Network for Pathological Gastric Cancer Segmentation—0
Coarse to Fine: Domain Adaptive Crowd Counting via Adversarial Scoring Network—0
A Reusable AI-Enabled Defect Detection System for Railway Using Ensembled CNN—0
A first step towards automated species recognition from camera trap images of mammals using AI in a European temperate forest—0
Coarse-To-Fine And Cross-Lingual ASR Transfer—0
CNN-Transformer Rectified Collaborative Learning for Medical Image Segmentation—0
CNN-LSTM and Transfer Learning Models for Malware Classification based on Opcodes and API Calls—0
CNN-based Survival Model for Pancreatic Ductal Adenocarcinoma in Medical Imaging—0
CNN Based Flank Predictor for Quadruped Animal Species—0
CNN Based Detection of Cardiovascular Diseases from ECG Images—0
A resource-efficient method for repeated HPO and NAS problems—0
Affordance Labeling and Exploration: A Manifold-Based Approach—0
Active Transfer Learning for Persian Offline Signature Verification—0
CNN based dense underwater 3D scene reconstruction by transfer learning using bubble database—0
CNN-based approach for glaucoma diagnosis using transfer learning and LBP-based data augmentation—0
A relic sketch extraction framework based on detail-aware hierarchical deep network—0
Affect inTweets: A Transfer Learning Approach—0
CMTA: COVID-19 Misinformation Multilingual Analysis on Twitter—0
CM3T: Framework for Efficient Multimodal Learning for Inhomogeneous Interaction Datasets—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