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 3351–3375 of 10307 papers

TitleStatusHype
Cross-domain and Cross-dimension Learning for Image-to-Graph TransformersCode0
Towards In-Vehicle Multi-Task Facial Attribute Recognition: Investigating Synthetic Data and Vision Foundation Models—0
Large Language Models on Fine-grained Emotion Detection Dataset with Data Augmentation and Transfer Learning—0
Multimodal deep learning approach to predicting neurological recovery from coma after cardiac arrest—0
Towards generalization of drug response prediction to single cells and patients utilizing importance-aware multi-source domain transfer learningCode0
Synthetic data generation for system identification: leveraging knowledge transfer from similar systemsCode0
Authorship Attribution in Bangla Literature (AABL) via Transfer Learning using ULMFiT—0
JointMotion: Joint Self-Supervision for Joint Motion Prediction—0
Hybridized Convolutional Neural Networks and Long Short-Term Memory for Improved Alzheimer's Disease Diagnosis from MRI Scans—0
Cell reprogramming design by transfer learning of functional transcriptional networksCode0
DA-Net: A Disentangled and Adaptive Network for Multi-Source Cross-Lingual Transfer Learning—0
Source Matters: Source Dataset Impact on Model Robustness in Medical ImagingCode0
Physics-informed and Unsupervised Riemannian Domain Adaptation for Machine Learning on Heterogeneous EEG Datasets—0
A Privacy-Preserving Framework with Multi-Modal Data for Cross-Domain Recommendation—0
Multi-modal Deep Learning—0
Self and Mixed Supervision to Improve Training Labels for Multi-Class Medical Image Segmentation—0
On Transfer in Classification: How Well do Subsets of Classes Generalize?—0
Neural Architecture Search using Particle Swarm and Ant Colony Optimization—0
Temporal Relations of Informative Frames in Action RecognitionCode0
PalmProbNet: A Probabilistic Approach to Understanding Palm Distributions in Ecuadorian Tropical Forest via Transfer Learning—0
Domain-Agnostic Mutual Prompting for Unsupervised Domain Adaptation—0
Zero-Shot Cross-Lingual Document-Level Event Causality Identification with Heterogeneous Graph Contrastive Transfer Learning—0
Encodings for Prediction-based Neural Architecture SearchCode0
A New Perspective on Smiling and Laughter Detection: Intensity Levels Matter—0
Distilled ChatGPT Topic & Sentiment Modeling with Applications in Finance—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