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 1651–1675 of 10307 papers

TitleStatusHype
A Review of Deep Transfer Learning and Recent Advancements—0
A Review of Deep Learning Approaches for Non-Invasive Cognitive Impairment Detection—0
A Survey of Fish Tracking Techniques Based on Computer Vision—0
A Flexible Selection Scheme for Minimum-Effort Transfer Learning—0
Active Transfer Learning Network: A Unified Deep Joint Spectral-Spatial Feature Learning Model For Hyperspectral Image Classification—0
Computational strategies for cross-species knowledge transfer and translational biomedicine—0
Compute- and Memory-Efficient Reinforcement Learning with Latent Experience Replay—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
A first step towards automated species recognition from camera trap images of mammals using AI in a European temperate forest—0
A Reusable AI-Enabled Defect Detection System for Railway Using Ensembled CNN—0
Active Transfer Learning for Persian Offline Signature Verification—0
Comprehensive performance comparison among different types of features in data-driven battery state of health estimation—0
Affordance Labeling and Exploration: A Manifold-Based Approach—0
Robust Evolutionary Multi-Objective Network Architecture Search for Reinforcement Learning (EMNAS-RL)—0
A resource-efficient method for repeated HPO and NAS problems—0
Comprehensive Lung Disease Detection Using Deep Learning Models and Hybrid Chest X-ray Data with Explainable AI—0
A relic sketch extraction framework based on detail-aware hierarchical deep network—0
Affect inTweets: A Transfer Learning Approach—0
Acceleration of Grokking in Learning Arithmetic Operations via Kolmogorov-Arnold Representation—0
AffecThor at SemEval-2018 Task 1: A cross-linguistic approach to sentiment intensity quantification in tweets—0
Neuronal and structural differentiation in the emergence of abstract rules in hierarchically modulated spiking neural networks—0
Active shooter detection and robust tracking utilizing supplemental synthetic data—0
A Recurrent Neural Network Approach to the Answering Machine Detection Problem—0
A Real Use Case of Semi-Supervised Learning for Mammogram Classification in a Local Clinic of Costa Rica—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