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 3526–3550 of 10307 papers

TitleStatusHype
Computational strategies for cross-species knowledge transfer and translational biomedicine—0
Compression of Higher Order Ambisonics with Multichannel RVQGAN—0
Adaptable image quality assessment using meta-reinforcement learning of task amenability—0
ACES -- Automatic Configuration of Energy Harvesting Sensors with Reinforcement Learning—0
Comprehensive performance comparison among different types of features in data-driven battery state of health estimation—0
Comprehensive Lung Disease Detection Using Deep Learning Models and Hybrid Chest X-ray Data with Explainable AI—0
Comprehensive and Comparative Analysis between Transfer Learning and Custom Built VGG and CNN-SVM Models for Wildfire Detection—0
A General Approach to Domain Adaptation with Applications in Astronomy—0
Compositional Zero-Shot Domain Transfer with Text-to-Text Models—0
Compositional Models: Multi-Task Learning and Knowledge Transfer with Modular Networks—0
A Siamese Neural Network with Modified Distance Loss For Transfer Learning in Speech Emotion Recognition—0
Time-Frequency Analysis based Deep Interference Classification for Frequency Hopping System—0
A general approach to bridge the reality-gap—0
Adaptable Automation with Modular Deep Reinforcement Learning and Policy Transfer—0
Composing Task-Agnostic Policies with Deep Reinforcement Learning—0
Composable Sparse Fine-Tuning for Cross-Lingual Transfer—0
A serial dual-channel library occupancy detection system based on Faster RCNN—0
A Sequential Self Teaching Approach for Improving Generalization in Sound Event Recognition—0
Age and Gender Prediction using Deep CNNs and Transfer Learning—0
Complying with the EU AI Act: Innovations in Explainable and User-Centric Hand Gesture Recognition—0
Complete Multilingual Neural Machine Translation—0
A Sequence Matching Network for Polyphonic Sound Event Localization and Detection—0
AGE2HIE: Transfer Learning from Brain Age to Predicting Neurocognitive Outcome for Infant Brain Injury—0
AdapNet: Adaptability Decomposing Encoder-Decoder Network for Weakly Supervised Action Recognition and Localization—0
A Centralized-Distributed Transfer Model for Cross-Domain Recommendation Based on Multi-Source Heterogeneous Transfer Learning—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