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 4501–4525 of 10307 papers

TitleStatusHype
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
Grounding Hierarchical Reinforcement Learning Models for Knowledge Transfer—0
Group-disentangled Representation Learning with Weakly-Supervised Regularization—0
Grouping-By-ID: Guarding Against Adversarial Domain Shifts—0
Group-Sparse Matrix Factorization for Transfer Learning of Word Embeddings—0
GROWN: GRow Only When Necessary for Continual Learning—0
GRSDet: Learning to Generate Local Reverse Samples for Few-shot Object Detection—0
GruPaTo at SemEval-2020 Task 12: Retraining mBERT on Social Media and Fine-tuned Offensive Language Models—0
GTA: Guided Transfer of Spatial Attention from Object-Centric Representations—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