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 8401–8450 of 10307 papers

TitleStatusHype
Parameter-Transferred Wasserstein Generative Adversarial Network (PT-WGAN) for Low-Dose PET Image DenoisingCode0
How to Not Measure Disentanglement—0
Deep Transfer Learning for Source Code ModelingCode0
Autonomous Navigation via Deep Reinforcement Learning for Resource Constraint Edge Nodes using Transfer LearningCode0
Model Fusion via Optimal TransportCode0
Green Deep Reinforcement Learning for Radio Resource Management: Architecture, Algorithm Compression and Challenge—0
On-chip Few-shot Learning with Surrogate Gradient Descent on a Neuromorphic Processor—0
Multi-modal Deep Analysis for Multimedia—0
Conversational Transfer Learning for Emotion Recognition—0
Automatic segmentation of texts into units of meaning for reading assistance—0
Aff-Wild Database and AffWildNetCode0
DDTCDR: Deep Dual Transfer Cross Domain Recommendation—0
Cross-lingual Alignment vs Joint Training: A Comparative Study and A Simple Unified FrameworkCode0
Learning protein conformational space by enforcing physics with convolutions and latent interpolations—0
A Closer Look At Feature Space Data Augmentation For Few-Shot Intent Classification—0
Imagined Value Gradients: Model-Based Policy Optimization with Transferable Latent Dynamics Models—0
HuggingFace's Transformers: State-of-the-art Natural Language ProcessingCode1
One-To-Many Multilingual End-to-end Speech Translation—0
ATL: Autonomous Knowledge Transfer from Many Streaming ProcessesCode0
Semi Few-Shot Attribute Translation—0
Linking emotions to behaviors through deep transfer learningCode0
FedMD: Heterogenous Federated Learning via Model DistillationCode1
Graph Few-shot Learning via Knowledge TransferCode0
Meta-Transfer Learning through Hard TasksCode1
Commonsense Knowledge Base Completion with Structural and Semantic ContextCode0
Semantic Preserving Generative Adversarial Models—0
Brain MRI Tumor Segmentation with Adversarial Networks—0
A Deep Learning System That Generates Quantitative CT Reports for Diagnosing Pulmonary Tuberculosis—0
Fine-grained Sentiment Classification using BERTCode0
Manufacturing Dispatching using Reinforcement and Transfer Learning—0
Revisiting Classical Bagging with Modern Transfer Learning for On-the-fly Disaster Damage Detector—0
Data-Efficient Goal-Oriented Conversation with Dialogue Knowledge Transfer Networks—0
Harnessing the Power of Infinitely Wide Deep Nets on Small-data TasksCode0
A Robust Transferable Deep Learning Framework for Cross-sectional Investment Strategy—0
Piracy Resistant Watermarks for Deep Neural NetworksCode0
Adversarially Robust Few-Shot Learning: A Meta-Learning ApproachCode0
DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighterCode1
Practical Deep Learning for Cloud, Mobile, and EdgeCode0
Generating Abstractive Summaries with Finetuned Language Models—0
MMAct: A Large-Scale Dataset for Cross Modal Human Action Understanding—0
Attention Bridging Network for Knowledge Transfer—0
Few-Shot Image Recognition With Knowledge TransferCode0
Self-Supervised Representation Learning From Multi-Domain Data—0
Deep Reinforcement Active Learning for Human-in-the-Loop Person Re-Identification—0
Application of Low-resource Machine Translation Techniques to Russian-Tatar Language Pair—0
Deep learning for Chemometric and non-translational dataCode0
A Survey of Methods to Leverage Monolingual Data in Low-resource Neural Machine Translation—0
Multilingual End-to-End Speech Translation—0
A Quantile-based Approach for Hyperparameter Transfer Learning—0
Generative One-Shot Face Recognition—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