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 6301–6325 of 10307 papers

TitleStatusHype
An Acceleration Method Based on Deep Learning and Multilinear Feature Space—0
Evaluation of Transfer Learning for Polish with a text-to-text model—0
Simplest Streaming TreesCode0
Learning Cooperation and Online Planning Through Simulation and Graph Convolutional Network—0
Knowledge Inheritance for Pre-trained Language Models—0
Lifelong Pretraining: Continually Adapting Language Models to Emerging Corpora—0
Cross-lingual Constituency Parsing with Linguistic Typology Knowledge—0
MTG: A Benchmarking Suite for Multilingual Text Generation—0
Towards Using Diachronic Distributed Word Representations as Models of Lexical Development—0
Scribosermo: Fast Speech-to-Text models for German and other LanguagesCode0
Improving Hyperparameter Optimization by Planning Ahead—0
Multilingual Speech Recognition using Knowledge Transfer across Learning Processes—0
Benchmark Problems for CEC2021 Competition on Evolutionary Transfer Multiobjectve OptimizationCode0
SPoT: Better Frozen Model Adaptation through Soft Prompt Transfer—0
Automatic Detection of COVID-19 and Pneumonia from Chest X-Ray using Deep Learning—0
Region Semantically Aligned Network for Zero-Shot Learning—0
Domain generalization in deep learning for contrast-enhanced imaging—0
Omni-Training: Bridging Pre-Training and Meta-Training for Few-Shot Learning—0
MedNet: Pre-trained Convolutional Neural Network Model for the Medical Imaging Tasks—0
Finding Materialized Models for Model ReuseCode0
Winning the ICCV'2021 VALUE Challenge: Task-aware Ensemble and Transfer Learning with Visual Concepts—0
Development of Deep Transformer-Based Models for Long-Term Prediction of Transient Production of Oil Wells—0
Småprat: DialoGPT for Natural Language Generation of Swedish Dialogue by Transfer Learning—0
Detecting Damage Building Using Real-time Crowdsourced Images and Transfer LearningCode0
Adapting TTS models For New Speakers using 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