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 6151–6200 of 10307 papers

TitleStatusHype
GLAMP: An Approximate Message Passing Framework for Transfer Learning with Applications to Lasso-based Estimators—0
GLID: Pre-training a Generalist Encoder-Decoder Vision Model—0
Glioma subtype classification from histopathological images using in-domain and out-of-domain transfer learning: An experimental study—0
Glitch Classification and Clustering for LIGO with Deep Transfer Learning—0
A knowledge transfer model for COVID-19 predicting and non-pharmaceutical intervention simulation—0
Global Estimation of Subsurface Eddy Kinetic Energy of Mesoscale Eddies Using a Multiple-input Residual Neural Network—0
Global Extreme Heat Forecasting Using Neural Weather Models—0
Global Flood Prediction: a Multimodal Machine Learning Approach—0
Global Weighted Average Pooling Bridges Pixel-level Localization and Image-level Classification—0
GLoMo: Unsupervised Learning of Transferable Relational Graphs—0
Glyph: Fast and Accurately Training Deep Neural Networks on Encrypted Data—0
GMP-TL: Gender-augmented Multi-scale Pseudo-label Enhanced Transfer Learning for Speech Emotion Recognition—0
GNN-based Precoder Design and Fine-tuning for Cell-free Massive MIMO with Real-world CSI—0
Goal-Oriented Chatbot Dialog Management Bootstrapping with Transfer Learning—0
Domain Adaptation with Joint Learning for Generic, Optical Car Part Recognition and Detection Systems (Go-CaRD)—0
Good Neighbors Are All You Need for Chinese Grapheme-to-Phoneme Conversion—0
GoodSAM: Bridging Domain and Capacity Gaps via Segment Anything Model for Distortion-aware Panoramic Semantic Segmentation—0
Good View Hunting: Learning Photo Composition From Dense View Pairs—0
Google is all you need: Semi-Supervised Transfer Learning Strategy For Light Multimodal Multi-Task Classification Model—0
GPS: Graph Contrastive Learning via Multi-scale Augmented Views from Adversarial Pooling—0
Adapting Amidst Degradation: Cross Domain Li-ion Battery Health Estimation via Physics-Guided Test-Time Training—0
Gradient-Based Automated Iterative Recovery for Parameter-Efficient Tuning—0
Gradient Sparsification For Masked Fine-Tuning of Transformers—0
Gradient Sparsification For Masked Fine-Tuning of Transformers—0
GradMix: Multi-source Transfer across Domains and Tasks—0
Gradually Vanishing Gap in Prototypical Network for Unsupervised Domain Adaptation—0
Gradual Tuning: a better way of Fine Tuning the parameters of a Deep Neural Network—0
Grafit: Learning fine-grained image representations with coarse labels—0
Grammatical vs Spelling Error Correction: An Investigation into the Responsiveness of Transformer-based Language Models using BART and MarianMT—0
Grapes disease detection using transfer learning—0
Graph Adaptive Knowledge Transfer for Unsupervised Domain Adaptation—0
Graph Attention Convolutional U-NET: A Semantic Segmentation Model for Identifying Flooded Areas—0
GraphControl: Adding Conditional Control to Universal Graph Pre-trained Models for Graph Domain Transfer Learning—0
Grapheme-to-Phoneme Transformer Model for Transfer Learning Dialects—0
Graph Enabled Cross-Domain Knowledge Transfer—0
Graph Neural Network based Child Activity Recognition—0
Graph Neural Network-based EEG Classification: A Survey—0
Graph Neural Networks for UnsupervisedDomain Adaptation of Histopathological ImageAnalytics—0
Graph Positional Autoencoders as Self-supervised Learners—0
Graph Pruning Based Spatial and Temporal Graph Convolutional Network with Transfer Learning for Traffic Prediction—0
Graph Sampling Based Deep Metric Learning for Generalizable Person Re-Identification—0
Graph schemas as abstractions for transfer learning, inference, and planning—0
Graph Self-Contrast Representation Learning—0
GraVIS: Grouping Augmented Views from Independent Sources for Dermatology Analysis—0
Greedy Structure Learning of Hierarchical Compositional Models—0
Green Deep Reinforcement Learning for Radio Resource Management: Architecture, Algorithm Compression and Challenge—0
Green Resource Allocation in Cloud-Native O-RAN Enabled Small Cell Networks—0
GridDehazeNet+: An Enhanced Multi-Scale Network with Intra-Task Knowledge Transfer for Single Image Dehazing—0
Gromov-Wasserstein Alignment of Word Embedding Spaces—0
Grounding Foundation Models through Federated Transfer Learning: A General Framework—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