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 3201–3225 of 10307 papers

TitleStatusHype
Supervised Contrastive Vision Transformer for Breast Histopathological Image Classification—0
Feature Corrective Transfer Learning: End-to-End Solutions to Object Detection in Non-Ideal Visual Conditions—0
GenFighter: A Generative and Evolutive Textual Attack Removal—0
Control Theoretic Approach to Fine-Tuning and Transfer Learning—0
Privacy-Enhanced Training-as-a-Service for On-Device Intelligence: Concept, Architectural Scheme, and Open Problems—0
Tao: Re-Thinking DL-based Microarchitecture Simulation—0
Lighter, Better, Faster Multi-Source Domain Adaptation with Gaussian Mixture Models and Optimal TransportCode0
Conditional Prototype Rectification Prompt LearningCode0
Self-Supervised Learning Featuring Small-Scale Image Dataset for Treatable Retinal Diseases Classification—0
High-Resolution Detection of Earth Structural Heterogeneities from Seismic Amplitudes using Convolutional Neural Networks with Attention layers—0
CREST: Cross-modal Resonance through Evidential Deep Learning for Enhanced Zero-Shot LearningCode0
FedDistill: Global Model Distillation for Local Model De-Biasing in Non-IID Federated Learning—0
Breast Cancer Image Classification Method Based on Deep Transfer Learning—0
Low-Resource Named Entity Recognition with Cross-Lingual, Character-Level Neural Conditional Random Fields—0
Intelligent Chemical Purification Technique Based on Machine Learning—0
Evaluating Fast Adaptability of Neural Networks for Brain-Computer InterfaceCode0
Rethinking Low-Rank Adaptation in Vision: Exploring Head-Level Responsiveness across Diverse Tasks—0
Advanced wood species identification based on multiple anatomical sections and using deep feature transfer and fusion—0
Investigating Neural Machine Translation for Low-Resource Languages: Using Bavarian as a Case StudyCode0
E3: Ensemble of Expert Embedders for Adapting Synthetic Image Detectors to New Generators Using Limited DataCode0
Transfer Learning Study of Motion Transformer-based Trajectory Predictions—0
Using Explainable AI and Transfer Learning to understand and predict the maintenance of Atlantic blocking with limited observational dataCode0
Convolutional neural network classification of cancer cytopathology images: taking breast cancer as an example—0
GLID: Pre-training a Generalist Encoder-Decoder Vision Model—0
MSciNLI: A Diverse Benchmark for Scientific Natural Language InferenceCode0
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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