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 6801–6825 of 10307 papers

TitleStatusHype
Database Workload Characterization with Query Plan EncodersCode0
NukeLM: Pre-Trained and Fine-Tuned Language Models for the Nuclear and Energy Domains—0
Boosting-GNN: Boosting Algorithm for Graph Networks on Imbalanced Node Classification—0
Transfer Learning and Curriculum Learning in Sokoban—0
Out-of-Distribution Detection in Dermatology using Input Perturbation and Subset Scanning—0
One4all User Representation for Recommender Systems in E-commerce—0
Pulmonary embolism identification in computerized tomography pulmonary angiography scans with deep learning technologies in COVID-19 patients—0
Fast Crack Detection Using Convolutional Neural Network—0
GAN pretraining for deep convolutional autoencoders applied to Software-based Fingerprint Presentation Attack DetectionCode0
Covid-19 Detection from Chest X-ray and Patient Metadata using Graph Convolutional Neural Networks—0
Light-weight Document Image Cleanup using Perceptual LossCode0
Efficient Transfer Learning via Joint Adaptation of Network Architecture and Weight—0
Unsupervised Discriminative Learning of Sounds for Audio Event Classification—0
Transfer learning approach to Classify the X-ray image that corresponds to corona disease Using ResNet50 pretrained by ChexNetCode0
Automatic Assessment of Alzheimer's Disease Diagnosis Based on Deep Learning TechniquesCode0
Exploring Driving-aware Salient Object Detection via Knowledge TransferCode0
Ensemble-based Transfer Learning for Low-resource Machine Translation Quality Estimation—0
Dermoscopic Image Classification with Neural Style Transfer—0
Deep Metric Learning for Few-Shot Image Classification: A Review of Recent Developments—0
Transfer Learning Enhanced Generative Adversarial Networks for Multi-Channel MRI ReconstructionCode0
SLGPT: Using Transfer Learning to Directly Generate Simulink Model Files and Find Bugs in the Simulink ToolchainCode0
Analysis and Prediction of NLP models via Task Embeddings—0
TAR: Generalized Forensic Framework to Detect Deepfakes using Weakly Supervised LearningCode0
TransferI2I: Transfer Learning for Image-to-Image Translation from Small Datasets—0
Multilingual Offensive Language Identification for Low-resource Languages—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