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 5751–5775 of 10307 papers

TitleStatusHype
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
The NTNU Taiwanese ASR System for Formosa Speech Recognition Challenge 2020—0
Gap Minimization for Knowledge Sharing and Transfer—0
GARCIA: Powering Representations of Long-tail Query with Multi-granularity Contrastive Learning—0
Gastrointestinal Disorder Detection with a Transformer Based Approach—0
Gastrointestinal Mucosal Problems Classification with Deep Learning—0
Security of Deep Learning Methodologies: Challenges and Opportunities—0
Gated Recurrent Neural Network Approach for Multilabel Emotion Detection in Microblogs—0
Gated Transfer Network for Transfer Learning—0
Security Vulnerability Detection Using Deep Learning Natural Language Processing—0
Gaussian Process Classification with Privileged Information by Soft-to-Hard Labeling Transfer—0
Gaussian Process Decentralized Data Fusion Meets Transfer Learning in Large-Scale Distributed Cooperative Perception—0
Gaussian Process Models for Link Analysis and Transfer Learning—0
Gaze-based Attention Recognition for Human-Robot Collaboration—0
Gaze-Net: Appearance-Based Gaze Estimation using Capsule Networks—0
GBSS:a global building semantic segmentation dataset for large-scale remote sensing building extraction—0
GCDH@LT-EDI-EACL2021: XLM-RoBERTa for Hope Speech Detection in English, Malayalam, and Tamil—0
GCN-RL Circuit Designer: Transferable Transistor Sizing with Graph Neural Networks and Reinforcement Learning—0
A Survey of GPT-3 Family Large Language Models Including ChatGPT and GPT-4—0
A Survey of Geometric Optimization for Deep Learning: From Euclidean Space to Riemannian Manifold—0
A Survey of Deep Visual Cross-Domain Few-Shot Learning—0
GenAINet: Enabling Wireless Collective Intelligence via Knowledge Transfer and Reasoning—0
Gendec: A Machine Learning-based Framework for Gender Detection from Japanese Names—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