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 1551–1575 of 10307 papers

TitleStatusHype
Adaptation and Re-Identification Network: An Unsupervised Deep Transfer Learning Approach to Person Re-Identification—0
A Simple yet Effective Joint Training Method for Cross-Lingual Universal Dependency Parsing—0
A General Multiple Data Augmentation Based Framework for Training Deep Neural Networks—0
An Explainable Vision Transformer with Transfer Learning Combined with Support Vector Machine Based Efficient Drought Stress Identification—0
Collaborative Recommendation with Auxiliary Data: A Transfer Learning View—0
Collusion Detection with Graph Neural Networks—0
A general method for regularizing tensor decomposition methods via pseudo-data—0
A generalized machine learning framework for brittle crack problems using transfer learning and graph neural networks—0
Adapt and Align to Improve Zero-Shot Sketch-Based Image Retrieval—0
A General Class of Transfer Learning Regression without Implementation Cost—0
ACES -- Automatic Configuration of Energy Harvesting Sensors with Reinforcement Learning—0
Adaptable image quality assessment using meta-reinforcement learning of task amenability—0
Collaborative Knowledge Fusion: A Novel Approach for Multi-task Recommender Systems via LLMs—0
Learning with Shared Representations: Statistical Rates and Efficient Algorithms—0
A General Approach to Domain Adaptation with Applications in Astronomy—0
A Siamese Neural Network with Modified Distance Loss For Transfer Learning in Speech Emotion Recognition—0
A general approach to bridge the reality-gap—0
Adaptable Automation with Modular Deep Reinforcement Learning and Policy Transfer—0
2M-NER: Contrastive Learning for Multilingual and Multimodal NER with Language and Modal Fusion—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
AGE2HIE: Transfer Learning from Brain Age to Predicting Neurocognitive Outcome for Infant Brain Injury—0
A Sequence Matching Network for Polyphonic Sound Event Localization and Detection—0
AdapNet: Adaptability Decomposing Encoder-Decoder Network for Weakly Supervised Action Recognition and Localization—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