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 8301–8350 of 10307 papers

TitleStatusHype
Bayesian Discovery of Multiple Bayesian Networks via Transfer Learning—0
Bayesian Experience Reuse for Learning from Multiple Demonstrators—0
Bayesian Generative Models for Knowledge Transfer in MRI Semantic Segmentation Problems—0
Bayesian Joint Modelling for Object Localisation in Weakly Labelled Images—0
Bayesian Knowledge Transfer for a Kalman Fixed-Lag Interval Smoother—0
Bayesian Model Adaptation for Crowd Counts—0
Bayesian multi-domain learning for cancer subtype discovery from next-generation sequencing count data—0
Continual Learning using a Bayesian Nonparametric Dictionary of Weight Factors—0
Bayesian Optimization of Bilevel Problems—0
Bayesian Physics-informed Neural Networks for System Identification of Inverter-dominated Power Systems—0
Bayesian Transfer Learning—0
Bayesian Transfer Learning: An Overview of Probabilistic Graphical Models for Transfer Learning—0
Bayesian Transfer Reinforcement Learning with Prior Knowledge Rules—0
Bazinga! A Dataset for Multi-Party Dialogues Structuring—0
BCNet: A Deep Convolutional Neural Network for Breast Cancer Grading—0
BD-KD: Balancing the Divergences for Online Knowledge Distillation—0
BEA-Base: A Benchmark for ASR of Spontaneous Hungarian—0
BEA-Base: A Benchmark for ASR of Spontaneous Hungarian—0
Beam Selection in ISAC using Contextual Bandit with Multi-modal Transformer and Transfer Learning—0
Behavior Priors for Efficient Reinforcement Learning—0
Being Generous with Sub-Words towards Small NMT Children—0
Belief Tree Search for Active Object Recognition—0
Benchmark data to study the influence of pre-training on explanation performance in MR image classification—0
Benchmarking Algorithms for Automatic License Plate Recognition—0
Exploring convolutional neural networks with transfer learning for diagnosing Lyme disease from skin lesion images—0
Benchmarking Deep Learning Frameworks for Automated Diagnosis of Ocular Toxoplasmosis: A Comprehensive Approach to Classification and Segmentation—0
Benchmarking Image Embeddings for E-Commerce: Evaluating Off-the Shelf Foundation Models, Fine-Tuning Strategies and Practical Trade-offs—0
Benchmarking of Lightweight Deep Learning Architectures for Skin Cancer Classification using ISIC 2017 Dataset—0
Benchmark Performance of Machine And Deep Learning Based Methodologies for Urdu Text Document Classification—0
Benchmarks and models for entity-oriented polarity detection—0
BERT Fine-Tuning for Sentiment Analysis on Indonesian Mobile Apps Reviews—0
BERT-PersNER: A New Model for Persian Named Entity Recognition—0
BERT Transformer model for Detecting Arabic GPT2 Auto-Generated Tweets—0
BeST -- A Novel Source Selection Metric for Transfer Learning—0
Best Arm Identification under Additive Transfer Bandits—0
Best of Both Worlds: Robust Accented Speech Recognition with Adversarial Transfer Learning—0
Best Practices for Learning Domain-Specific Cross-Lingual Embeddings—0
Best Practices in Convolutional Networks for Forward-Looking Sonar Image Recognition—0
Better and Faster: Knowledge Transfer from Multiple Self-supervised Learning Tasks via Graph Distillation for Video Classification—0
Better Transfer Learning with Inferred Successor Maps—0
Between-Domain Instance Transition Via the Process of Gibbs Sampling in RBM—0
BEV-Seg: Bird's Eye View Semantic Segmentation Using Geometry and Semantic Point Cloud—0
Beyond Efficiency: Molecular Data Pruning for Enhanced Generalization—0
Beyond Fine Tuning: A Modular Approach to Learning on Small Data—0
Beyond Fine-tuning: Classifying High Resolution Mammograms using Function-Preserving Transformations—0
Beyond Flatland: Pre-training with a Strong 3D Inductive Bias—0
Beyond Glucose-Only Assessment: Advancing Nocturnal Hypoglycemia Prediction in Children with Type 1 Diabetes—0
Beyond H-Divergence: Domain Adaptation Theory With Jensen-Shannon Divergence—0
Beyond Model Scale Limits: End-Edge-Cloud Federated Learning with Self-Rectified Knowledge Agglomeration—0
Beyond Not-Forgetting: Continual Learning with Backward Knowledge Transfer—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