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 2226–2250 of 10307 papers

TitleStatusHype
Building and Road Segmentation Using EffUNet and Transfer Learning Approach—0
Annotation-Free Cardiac Vessel Segmentation via Knowledge Transfer from Retinal Images—0
A Correlation-Ratio Transfer Learning and Variational Stein's Paradox—0
Decentralized Reinforcement Learning: Global Decision-Making via Local Economic Transactions—0
Deception Detection with Feature-Augmentation by soft Domain Transfer—0
Building Advanced Dialogue Managers for Goal-Oriented Dialogue Systems—0
Multihop: Leveraging Complex Models to Learn Accurate Simple Models—0
Dealing with Adversarial Player Strategies in the Neural Network Game iNNk through Ensemble Learning—0
BugWhisperer: Fine-Tuning LLMs for SoC Hardware Vulnerability Detection—0
Advanced Lung Nodule Segmentation and Classification for Early Detection of Lung Cancer using SAM and Transfer Learning—0
DEArt: Dataset of European Art—0
Bristle: Decentralized Federated Learning in Byzantine, Non-i.i.d. Environments—0
A Corpus for Commonsense Inference in Story Cloze Test—0
1-D Convolutional Graph Convolutional Networks for Fault Detection in Distributed Energy Systems—0
An Iterative Transfer Learning Based Ensemble Technique for Automatic Short Answer Grading—0
(DE)^2 CO: Deep Depth Colorization—0
Dec-Adapter: Exploring Efficient Decoder-Side Adapter for Bridging Screen Content and Natural Image Compression—0
Bridging The Multi-Modality Gaps of Audio, Visual and Linguistic for Speech Enhancement—0
Bridging the Gap: Transfer Learning from English PLMs to Malaysian English—0
An Iterative Similarity based Adaptation Technique for Cross-domain Text Classification—0
An Iterative Knowledge Transfer NMT System for WMT20 News Translation Task—0
Bridging the Bosphorus: Advancing Turkish Large Language Models through Strategies for Low-Resource Language Adaptation and Benchmarking—0
Advanced Deep Learning and Large Language Models: Comprehensive Insights for Cancer Detection—0
Evolutionary Optimization of 1D-CNN for Non-contact Respiration Pattern Classification—0
Bridging Modalities: Enhancing Cross-Modality Hate Speech Detection with Few-Shot In-Context 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