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 5401–5425 of 10307 papers

TitleStatusHype
NodeTrans: A Graph Transfer Learning Approach for Traffic Prediction—0
Classification of Alzheimer's Disease Using the Convolutional Neural Network (CNN) with Transfer Learning and Weighted Loss—0
A Robust Ensemble Model for Patasitic Egg Detection and Classification—0
Boosting Single-Frame 3D Object Detection by Simulating Multi-Frame Point Clouds—0
Mental Illness Classification on Social Media Texts using Deep Learning and Transfer Learning—0
Unified Object Detector for Different Modalities based on Vision TransformersCode0
Transfer Learning and Masked Generation for Answer Verbalization—0
Training Novices: The Role of Human-AI Collaboration and Knowledge Transfer—0
The Specificity and Helpfulness of Peer-to-Peer Feedback in Higher Education—0
Are You Really Okay? A Transfer Learning-based Approach for Identification of Underlying Mental Illnesses—0
kpfriends at SemEval-2022 Task 2: NEAMER - Named Entity Augmented Multi-word Expression Recognizer—0
LastResort at SemEval-2022 Task 5: Towards Misogyny Identification using Visual Linguistic Model Ensembles And Task-Specific Pretraining—0
Aligning Generative Language Models with Human Values—0
Harmless Transfer Learning for Item Embeddings—0
Pruning Adatperfusion with Lottery Ticket Hypothesis—0
A Systematic Survey of Text Worlds as Embodied Natural Language Environments—0
PINGAN Omini-Sinitic at SemEval-2022 Task 4: Multi-prompt Training for Patronizing and Condescending Language Detection—0
The Importance of the Instantaneous Phase for classification using Convolutional Neural Networks—0
Language Models for Code-switch Detection of te reo Māori and English in a Low-resource Setting—0
Knowledge Transfer between Structured and Unstructured Sources for Complex Question Answering—0
Geographical Distance Is The New Hyperparameter: A Case Study Of Finding The Optimal Pre-trained Language For English-isiZulu Machine Translation.—0
Team Innovators at SemEval-2022 for Task 8: Multi-Task Training with Hyperpartisan and Semantic Relation for Multi-Lingual News Article Similarity—0
LingJing at SemEval-2022 Task 3: Applying DeBERTa to Lexical-level Presupposed Relation Taxonomy with Knowledge TransferCode0
Free speech or Free Hate Speech? Analyzing the Proliferation of Hate Speech in Parler—0
COVID-19 Detection Using Transfer Learning Approach from Computed Tomography ImagesCode0
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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