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 6676–6700 of 10307 papers

TitleStatusHype
Secost: Sequential co-supervision for large scale weakly labeled audio event detection—0
Secure Federated Transfer Learning—0
SecureNet: A Comparative Study of DeBERTa and Large Language Models for Phishing Detection—0
Secure Transfer Learning: Training Clean Models Against Backdoor in (Both) Pre-trained Encoders and Downstream Datasets—0
Bilingual Character Representation for Efficiently Addressing Out-of-Vocabulary Words in Code-Switching Named Entity Recognition—0
Security of Deep Learning Methodologies: Challenges and Opportunities—0
Security Vulnerability Detection Using Deep Learning Natural Language Processing—0
Bilingual Language Modeling, A transfer learning technique for Roman Urdu—0
Bilingual Transfer Learning for Online Product Classification—0
Seed Phenotyping on Neural Networks using Domain Randomization and Transfer Learning—0
Binary Classification of Alzheimer Disease using sMRI Imaging modality and Deep Learning—0
Seg4Reg+: Consistency Learning between Spine Segmentation and Cobb Angle Regression—0
Binary Paragraph Vectors—0
SegBook: A Simple Baseline and Cookbook for Volumetric Medical Image Segmentation—0
Segmentation Framework for Heat Loss Identification in Thermal Images: Empowering Scottish Retrofitting and Thermographic Survey Companies—0
Segmentation of Mental Foramen in Orthopantomographs: A Deep Learning Approach—0
Segmentation of Shoulder Muscle MRI Using a New Region and Edge based Deep Auto-Encoder—0
Segmenting across places: The need for fair transfer learning with satellite imagery—0
BiNet: Degraded-Manuscript Binarization in Diverse Document Textures and Layouts using Deep Encoder-Decoder Networks—0
Adapter Pruning using Tropical Characterization—0
Select and Distill: Selective Dual-Teacher Knowledge Transfer for Continual Learning on Vision-Language Models—0
Selecting Subsets of Source Data for Transfer Learning with Applications in Metal Additive Manufacturing—0
BioAMA: Towards an End to End BioMedical Question Answering System—0
BioBERTpt - A Portuguese Neural Language Model for Clinical Named Entity Recognition—0
SelectiveFinetuning: Enhancing Transfer Learning in Sleep Staging through Selective Domain Alignment—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