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 4751–4800 of 10307 papers

TitleStatusHype
Accelerating hydrodynamic simulations of urban drainage systems with physics-guided machine learning—0
Linear Connectivity Reveals Generalization StrategiesCode1
Face2Text revisited: Improved data set and baseline results—0
Hyper-X: A Unified Hypernetwork for Multi-Task Multilingual TransferCode1
On statistic alignment for domain adaptation in structural health monitoringCode0
Evaluating the Impact of Model Scale for Compositional Generalization in Semantic Parsing—0
ATTEMPT: Parameter-Efficient Multi-task Tuning via Attentional Mixtures of Soft PromptsCode1
Thalamus: a brain-inspired algorithm for biologically-plausible continual learning and disentangled representationsCode1
When does Parameter-Efficient Transfer Learning Work for Machine Translation?Code0
Cross-lingual Lifelong LearningCode0
Global Extreme Heat Forecasting Using Neural Weather Models—0
Paddy Doctor: A Visual Image Dataset for Automated Paddy Disease Classification and Benchmarking—0
The Geometry of Multilingual Language Model RepresentationsCode1
muNet: Evolving Pretrained Deep Neural Networks into Scalable Auto-tuning Multitask Systems—0
Classification of Quasars, Galaxies, and Stars in the Mapping of the Universe Multi-modal Deep LearningCode0
Vision Transformers in 2022: An Update on Tiny ImageNetCode1
Action Recognition for American Sign Language—0
Pre-Train Your Loss: Easy Bayesian Transfer Learning with Informative PriorsCode1
SafeNet: The Unreasonable Effectiveness of Ensembles in Private Collaborative Learning—0
Deep transfer learning for image classification: a survey—0
Human Gender Prediction Based on Deep Transfer Learning from Panoramic Radiograph Images—0
EXPANSE: A Deep Continual / Progressive Learning System for Deep Transfer LearningCode0
Dexterous Robotic Manipulation using Deep Reinforcement Learning and Knowledge Transfer for Complex Sparse Reward-based TasksCode0
Robust and Efficient Medical Imaging with Self-SupervisionCode3
Causal Inference from Small High-dimensional Datasets—0
TransTab: Learning Transferable Tabular Transformers Across TablesCode2
Persian Natural Language Inference: A Meta-learning approachCode0
Federated learning: Applications, challenges and future directions—0
Evaluation of Transfer Learning for Polish with a Text-to-Text Model—0
Global Contrast Masked Autoencoders Are Powerful Pathological Representation LearnersCode1
Label-Efficient Self-Supervised Federated Learning for Tackling Data Heterogeneity in Medical ImagingCode1
Quantum Transfer Learning for Wi-Fi Sensing—0
Geographical Distance Is The New Hyperparameter: A Case Study Of Finding The Optimal Pre-trained Language For English-isiZulu Machine TranslationCode0
A unified framework for dataset shift diagnosticsCode1
HelixADMET: a robust and endpoint extensible ADMET system incorporating self-supervised knowledge transfer—0
When to Use Multi-Task Learning vs Intermediate Fine-Tuning for Pre-Trained Encoder Transfer LearningCode0
Manifold Characteristics That Predict Downstream Task PerformanceCode0
Fused Deep Neural Network based Transfer Learning in Occluded Face Classification and Person re-Identification—0
Classification of Astronomical Bodies by Efficient Layer Fine-Tuning of Deep Neural NetworksCode0
Improving Neural Machine Translation of Indigenous Languages with Multilingual Transfer Learning—0
Efficient Deep Learning Methods for Identification of Defective Casting ProductsCode0
Revisiting Facial Key Point Detection: An Efficient Approach Using Deep Neural Networks—0
How to Fine-tune Models with Few Samples: Update, Data Augmentation, and Test-time AugmentationCode0
Toward a Geometrical Understanding of Self-supervised Contrastive Learning—0
A Comprehensive Survey of Few-shot Learning: Evolution, Applications, Challenges, and Opportunities—0
Exploring the structure-property relations of thin-walled, 2D extruded lattices using neural networks—0
Exploiting Variational Domain-Invariant User Embedding for Partially Overlapped Cross Domain Recommendation—0
SimCPSR: Simple Contrastive Learning for Paper Submission Recommendation SystemCode0
D3T-GAN: Data-Dependent Domain Transfer GANs for Few-shot Image Generation—0
DTW at Qur'an QA 2022: Utilising Transfer Learning with Transformers for Question Answering in a Low-resource DomainCode0
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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