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 7226–7250 of 10307 papers

TitleStatusHype
Skin Cancer Images Classification using Transfer Learning Techniques—0
LinkSAGE: Optimizing Job Matching Using Graph Neural Networks—0
Skin cancer reorganization and classification with deep neural network—0
A network-based transfer learning approach to improve sales forecasting of new products—0
LION: Implicit Vision Prompt Tuning—0
LipidBERT: A Lipid Language Model Pre-trained on METiS de novo Lipid Library—0
An Ensemble Model for Distorted Images in Real Scenarios—0
LIRMM-Advanse at SemEval-2019 Task 3: Attentive Conversation Modeling for Emotion Detection and Classification—0
Listening to the World Improves Speech Command Recognition—0
Efficient Systematic Reviews: Literature Filtering with Transformers & Transfer Learning—0
Skin Disease Detection and Classification of Actinic Keratosis and Psoriasis Utilizing Deep Transfer Learning—0
Live American Sign Language Letter Classification with Convolutional Neural Networks—0
Skin Lesion Analyser: An Efficient Seven-Way Multi-Class Skin Cancer Classification Using MobileNet—0
Liver Steatosis Segmentation with Deep Learning Methods—0
An ensemble-based approach by fine-tuning the deep transfer learning models to classify pneumonia from chest X-ray images—0
A Comparative Study of Open Source Computer Vision Models for Application on Small Data: The Case of CFRP Tape Laying—0
LLEDA -- Lifelong Self-Supervised Domain Adaptation—0
An Ensemble Approach to Personalized Real Time Predictive Writing for Experts—0
LLM4WM: Adapting LLM for Wireless Multi-Tasking—0
An Enhancement of CNN Algorithm for Rice Leaf Disease Image Classification in Mobile Applications—0
LLM-KT: A Versatile Framework for Knowledge Transfer from Large Language Models to Collaborative Filtering—0
An End-to-End Mispronunciation Detection System for L2 English Speech Leveraging Novel Anti-Phone Modeling—0
LLMs Are Globally Multilingual Yet Locally Monolingual: Exploring Knowledge Transfer via Language and Thought Theory—0
LLM-USO: Large Language Model-based Universal Sizing Optimizer—0
SKoPe3D: A Synthetic Dataset for Vehicle Keypoint Perception in 3D from Traffic Monitoring Cameras—0
Show:102550
← PrevPage 290 of 413Next →
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