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 8501–8525 of 10307 papers

TitleStatusHype
Palomino-Ochoa at SemEval-2020 Task 9: Robust System based on Transformer for Code-Mixed Sentiment Classification—0
Adversarial Multitask Learning for Joint Multi-Feature and Multi-Dialect Morphological Modeling—0
Adversarial Multi-Source Transfer Learning in Healthcare: Application to Glucose Prediction for Diabetic People—0
Adversarial Multi-Agent Reinforcement Learning for Proactive False Data Injection Detection—0
Adversarial Meta Sampling for Multilingual Low-Resource Speech Recognition—0
Adversarial Inductive Transfer Learning with input and output space adaptation—0
Pandora: A Code-Driven Large Language Model Agent for Unified Reasoning Across Diverse Structured Knowledge—0
PANLP at MEDIQA 2019: Pre-trained Language Models, Transfer Learning and Knowledge Distillation—0
Adversarial Imitation via Variational Inverse Reinforcement Learning—0
PanoSwin: a Pano-style Swin Transformer for Panorama Understanding—0
Paradox in Deep Neural Networks: Similar yet Different while Different yet Similar—0
Stylized Projected GAN: A Novel Architecture for Fast and Realistic Image Generation—0
Parallel Distributed Logistic Regression for Vertical Federated Learning without Third-Party Coordinator—0
Parallel Knowledge Transfer in Multi-Agent Reinforcement Learning—0
Parallel sentences mining with transfer learning in an unsupervised setting—0
Subdomain Adaptation with Manifolds Discrepancy Alignment—0
Subgraph Networks Based Contrastive Learning—0
Adversarial Fine-tune with Dynamically Regulated Adversary—0
Parameter-Efficient Abstractive Question Answering over Tables and over Text—0
A Big Data-empowered System for Real-time Detection of Regional Discriminatory Comments on Vietnamese Social Media—0
Parameter-Efficient and Student-Friendly Knowledge Distillation—0
Sub-network Discovery and Soft-masking for Continual Learning of Mixed Tasks—0
Parameter-efficient Dysarthric Speech Recognition Using Adapter Fusion and Householder Transformation—0
Parameter-Efficient Fine-Tuning for Medical Image Analysis: The Missed Opportunity—0
Adversarial Feature Training for Generalizable Robotic Visuomotor Control—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