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 8926–8950 of 10307 papers

TitleStatusHype
Swapped Face Detection using Deep Learning and Subjective Assessment—0
Swing Distillation: A Privacy-Preserving Knowledge Distillation Framework—0
3D U-Net for segmentation of COVID-19 associated pulmonary infiltrates using transfer learning: State-of-the-art results on affordable hardware—0
PSDNet: Determination of Particle Size Distributions Using Synthetic Soil Images and Convolutional Neural Networks—0
SWIPTNet: A Unified Deep Learning Framework for SWIPT based on GNN and Transfer Learning—0
A Data Augmented Approach to Transfer Learning for Covid-19 Detection—0
Regularizing CNN Transfer Learning with Randomised Regression—0
P-Swish: Activation Function with Learnable Parameters Based on Swish Activation Function in Deep Learning—0
Psychological Health Knowledge-Enhanced LLM-based Social Network Crisis Intervention Text Transfer Recognition Method—0
Psychological State in Text: A Limitation of Sentiment Analysis—0
psyML at SemEval-2018 Task 1: Transfer Learning for Sentiment and Emotion Analysis—0
AdarGCN: Adaptive Aggregation GCN for Few-Shot Learning—0
ADARES: Adaptive Resource Management for Virtual Machines—0
BugWhisperer: Fine-Tuning LLMs for SoC Hardware Vulnerability Detection—0
Multihop: Leveraging Complex Models to Learn Accurate Simple Models—0
Building Advanced Dialogue Managers for Goal-Oriented Dialogue Systems—0
Building and Road Segmentation Using EffUNet and Transfer Learning Approach—0
Adaptive Variants of Optimal Feedback Policies—0
Building a Question and Answer System for News Domain—0
Building a Winning Team: Selecting Source Model Ensembles using a Submodular Transferability Estimation Approach—0
Building Efficient Lightweight CNN Models—0
Building Height Prediction with Instance Segmentation—0
Building Inspection Toolkit: Unified Evaluation and Strong Baselines for Damage Recognition—0
Building medical image classifiers with very limited data using segmentation networks—0
Building Robust Industrial Applicable Object Detection Models Using Transfer Learning and Single Pass Deep Learning Architectures—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