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 5976–6000 of 10307 papers

TitleStatusHype
A Sequential Self Teaching Approach for Improving Generalization in Sound Event Recognition—0
Grounding Foundation Models through Federated Transfer Learning: A General Framework—0
Grounding Hierarchical Reinforcement Learning Models for Knowledge Transfer—0
A Sequence Matching Network for Polyphonic Sound Event Localization and Detection—0
Monocular Cyclist Detection with Convolutional Neural Networks—0
Group-disentangled Representation Learning with Weakly-Supervised Regularization—0
A Semi-supervised Approach to Generate the Code-Mixed Text using Pre-trained Encoder and Transfer Learning—0
Grouping-By-ID: Guarding Against Adversarial Domain Shifts—0
A Semiparametric Efficient Approach To Label Shift Estimation and Quantification—0
ActiLabel: A Combinatorial Transfer Learning Framework for Activity Recognition—0
Group-Sparse Matrix Factorization for Transfer Learning of Word Embeddings—0
A Semantics-Guided Class Imbalance Learning Model for Zero-Shot Classification—0
Self-learning Scene-specific Pedestrian Detectors using a Progressive Latent Model—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
Self-Normalizing Neural Network, Enabling One Shot Transfer Learning for Modeling EDFA Wavelength Dependent Gain—0
Guided Generative Adversarial Neural Network for Representation Learning and High Fidelity Audio Generation using Fewer Labelled Audio Data—0
A Self-attention Knowledge Domain Adaptation Network for Commercial Lithium-ion Batteries State-of-health Estimation under Shallow Cycles—0
Guided Recommendation for Model Fine-Tuning—0
Self-Regulated Data-Free Knowledge Amalgamation for Text Classification—0
Guillotine Regularization: Why removing layers is needed to improve generalization in Self-Supervised Learning—0
GUST: Quantifying Free-Form Geometric Uncertainty of Metamaterials Using Small Data—0
Self Regulated Learning Mechanism for Data Efficient Knowledge Distillation—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