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 8551–8600 of 10307 papers

TitleStatusHype
Classification of breast cancer histology images using transfer learning—0
Classification of Breast Cancer Lesions in Ultrasound Images by using Attention Layer and loss Ensembles in Deep Convolutional Neural Networks—0
Classification of Chest Diseases using Wavelet Transforms and Transfer Learning—0
Classification of Colorectal Cancer Polyps via Transfer Learning and Vision-Based Tactile Sensing—0
Classification of COVID-19 in Chest CT Images using Convolutional Support Vector Machines—0
Classification of COVID-19 Patients with their Severity Level from Chest CT Scans using Transfer Learning—0
Classification of Diabetic Retinopathy Using Unlabeled Data and Knowledge Distillation—0
Classification of Diabetic Retinopathy via Fundus Photography: Utilization of Deep Learning Approaches to Speed up Disease Detection—0
Classification of Shoulder X-Ray Images with Deep Learning Ensemble Models—0
Classification of Geographical Land Structure Using Convolution Neural Network and Transfer Learning—0
Classification of Human Monkeypox Disease Using Deep Learning Models and Attention Mechanisms—0
Classification of Industrial Control Systems screenshots using Transfer Learning—0
Classification of Luminal Subtypes in Full Mammogram Images Using Transfer Learning—0
Classification of Melanocytic Nevus Images using BigTransfer (BiT)—0
Classification of Microscopy Images of Breast Tissue: Region Duplication based Self-Supervision vs. Off-the Shelf Deep Representations—0
Classification of Skin Cancer Images using Convolutional Neural Networks—0
Classification of Skin Disease Using Transfer Learning in Convolutional Neural Networks—0
Classifying Documents within Multiple Hierarchical Datasets using Multi-Task Learning—0
Classifying Judgements using Transfer Learning—0
Class Relationship Embedded Learning for Source-Free Unsupervised Domain Adaptation—0
Classroom-Inspired Multi-Mentor Distillation with Adaptive Learning Strategies—0
Class Similarity-Based Multimodal Classification under Heterogeneous Category Sets—0
Class-Specific Channel Attention for Few-Shot Learning—0
Class-Specific Data Augmentation: Bridging the Imbalance in Multiclass Breast Cancer Classification—0
Class Subset Selection for Transfer Learning using Submodularity—0
CLCE: An Approach to Refining Cross-Entropy and Contrastive Learning for Optimized Learning Fusion—0
Cleaning tasks knowledge transfer between heterogeneous robots: a deep learning approach—0
CLEAR: Cumulative LEARning for One-Shot One-Class Image Recognition—0
CleverDistiller: Simple and Spatially Consistent Cross-modal Distillation—0
CLICKER: Attention-Based Cross-Lingual Commonsense Knowledge Transfer—0
Client Clustering Meets Knowledge Sharing: Enhancing Privacy and Robustness in Personalized Peer-to-Peer Learning—0
Cliff-Learning—0
Clinical Concept and Relation Extraction Using Prompt-based Machine Reading Comprehension—0
Clinical Document Classification Using Labeled and Unlabeled Data Across Hospitals—0
Clinical Risk Prediction Using Language Models: Benefits And Considerations—0
CLIP also Understands Text: Prompting CLIP for Phrase Understanding—0
CLIP-aware Domain-Adaptive Super-Resolution—0
CLIP-CID: Efficient CLIP Distillation via Cluster-Instance Discrimination—0
CLIP-FLow: Contrastive Learning by semi-supervised Iterative Pseudo labeling for Optical Flow Estimation—0
CLIP is Almost All You Need: Towards Parameter-Efficient Scene Text Retrieval without OCR—0
CLIP-S^4: Language-Guided Self-Supervised Semantic Segmentation—0
CLIP-S4: Language-Guided Self-Supervised Semantic Segmentation—0
Close Yet Distinctive Domain Adaptation—0
CloudifierNet -- Deep Vision Models for Artificial Image Processing—0
CloudRCA: A Root Cause Analysis Framework for Cloud Computing Platforms—0
CLUE: Contextualised Unified Explainable Learning of User Engagement in Video Lectures—0
ClueGAIN: Application of Transfer Learning On Generative Adversarial Imputation Nets (GAIN)—0
ClusMFL: A Cluster-Enhanced Framework for Modality-Incomplete Multimodal Federated Learning in Brain Imaging Analysis—0
Clustering-based Multitasking Deep Neural Network for Solar Photovoltaics Power Generation Prediction—0
Clustering Markov Decision Processes For Continual Transfer—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