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 3751–3775 of 10307 papers

TitleStatusHype
CLUE: Contextualised Unified Explainable Learning of User Engagement in Video Lectures—0
Active Sentiment Domain Adaptation—0
Acceleration of Grokking in Learning Arithmetic Operations via Kolmogorov-Arnold Representation—0
Exploration in Knowledge Transfer Utilizing Reinforcement Learning—0
Robust Evolutionary Multi-Objective Network Architecture Search for Reinforcement Learning (EMNAS-RL)—0
Exploration of Dark Chemical Genomics Space via Portal Learning: Applied to Targeting the Undruggable Genome and COVID-19 Anti-Infective Polypharmacology—0
Exploration of Various Deep Learning Models for Increased Accuracy in Automatic Polyp Detection—0
Explorative Curriculum Learning for Strongly Correlated Electron Systems—0
Temporal Transfer Learning for Traffic Optimization with Coarse-grained Advisory Autonomy—0
Experience Reuse with Probabilistic Movement Primitives—0
Exploring bat song syllable representations in self-supervised audio encoders—0
Exploring Benefits of Transfer Learning in Neural Machine Translation—0
Expectation maximization transfer learning and its application for bionic hand prostheses—0
Exploring CausalWorld: Enhancing robotic manipulation via knowledge transfer and curriculum learning—0
Exploring connections of spectral analysis and transfer learning in medical imaging—0
Exploring Cross-Lingual Transfer Learning with Unsupervised Machine Translation—0
ExpandNets: Linear Over-parameterization to Train Compact Convolutional Networks—0
CloudRCA: A Root Cause Analysis Framework for Cloud Computing Platforms—0
Expanding Expressiveness of Diffusion Models with Limited Data via Self-Distillation based Fine-Tuning—0
Expanding Deep Learning-based Sensing Systems with Multi-Source Knowledge Transfer—0
CloudifierNet -- Deep Vision Models for Artificial Image Processing—0
Exploring Fluent Query Reformulations with Text-to-Text Transformers and Reinforcement Learning—0
Arabic Text Diacritization In The Age Of Transfer Learning: Token Classification Is All You Need—0
A Feature Transfer Enabled Multi-Task Deep Learning Model on Medical Imaging—0
ExoMiner++: Enhanced Transit Classification and a New Vetting Catalog for 2-Minute TESS Data—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