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 8201–8250 of 10307 papers

TitleStatusHype
AutoBayes: Automated Bayesian Graph Exploration for Nuisance-Robust Inference—0
Auto-Compressing Networks—0
Autocorrect for Estonian texts: final report from project EKTB25—0
Autoencoder Based Sample Selection for Self-Taught Learning—0
Autoencoding Features for Aviation Machine Learning Problems—0
AutoFT: Automatic Fine-Tune for Parameters Transfer Learning in Click-Through Rate Prediction—0
Automated Audio Captioning using Transfer Learning and Reconstruction Latent Space Similarity Regularization—0
Automated Blood Cell Detection and Counting via Deep Learning for Microfluidic Point-of-Care Medical Devices—0
Automated Customization of On-Thing Inference for Quality-of-Experience Enhancement—0
Automated Detection of Coronary Artery Stenosis in X-ray Angiography using Deep Neural Networks—0
Automated Detection of Dolphin Whistles with Convolutional Networks and Transfer Learning—0
Automated detection of gibbon calls from passive acoustic monitoring data using convolutional neural networks in the "torch for R" ecosystem—0
Automated detection of oral pre-cancerous tongue lesions using deep learning for early diagnosis of oral cavity cancer—0
Automated diagnosis of lung diseases using vision transformer: a comparative study on chest x-ray classification—0
Data Augmentation for Automated Essay Scoring using Transformer Models—0
Automated Extraction of Fine-Grained Standardized Product Information from Unstructured Multilingual Web Data—0
Automated identification of neural cells in the multi-photon images using deep-neural networks—0
Automated Identification of Tree Species by Bark Texture Classification Using Convolutional Neural Networks—0
Automated Knot Detection and Pairing for Wood Analysis in the Timber Industry—0
Automated Multi-sequence Cardiac MRI Segmentation Using Supervised Domain Adaptation—0
Automated PII Extraction from Social Media for Raising Privacy Awareness: A Deep Transfer Learning Approach—0
Automated Pruning for Deep Neural Network Compression—0
Automated Scoring of Clinical Expressive Language Evaluation Tasks—0
Automated Segmentation and Analysis of Microscopy Images of Laser Powder Bed Fusion Melt Tracks—0
Automated Testing of Spatially-Dependent Environmental Hypotheses through Active Transfer Learning—0
Automated Tomato Maturity Estimation Using an Optimized Residual Model with Pruning and Quantization Techniques—0
Automated Visual Attention Detection using Mobile Eye Tracking in Behavioral Classroom Studies—0
Automatically Predict Material Properties with Microscopic Image Example Polymer Compatibility—0
Automatically Score Tissue Images Like a Pathologist by Transfer Learning—0
Automatic Audio Captioning using Attention weighted Event based Embeddings—0
Automatic Colon Polyp Detection using Region based Deep CNN and Post Learning Approaches—0
Automatic Control of Reactive Brain Computer Interfaces—0
Automatic Organization of Neural Modules for Enhanced Collaboration in Neural Networks—0
Automatic Detection of COVID-19 and Pneumonia from Chest X-Ray using Deep Learning—0
Automatic detection of rare pathologies in fundus photographs using few-shot learning—0
Automatic detection of passable roads after floods in remote sensed and social media data—0
Automatic Diagnosis of COVID-19 from CT Images using CycleGAN and Transfer Learning—0
Automatic Discovery of Novel Intents & Domains from Text Utterances—0
Automatic extraction of coronary arteries using deep learning in invasive coronary angiograms—0
Automatic Graphic Logo Detection via Fast Region-based Convolutional Networks—0
Automatic Labeling of Data for Transfer Learning—0
Automatic lesion segmentation and Pathological Myopia classification in fundus images—0
Automatic Lung Cancer Prediction from Chest X-ray Images Using Deep Learning Approach—0
Automatic Mass Detection in Breast Using Deep Convolutional Neural Network and SVM Classifier—0
Automatic phantom test pattern classification through transfer learning with deep neural networks—0
Automatic Plaque Detection in IVOCT Pullbacks Using Convolutional Neural Networks—0
Automatic Recognition of Coal and Gangue based on Convolution Neural Network—0
Automatic Recognition of Food Ingestion Environment from the AIM-2 Wearable Sensor—0
Automatic Recognition of the Supraspinatus Tendinopathy from Ultrasound Images using Convolutional Neural Networks—0
Novel coronavirus pneumonia lesion segmentation in CT images—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