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 7876–7900 of 10307 papers

TitleStatusHype
Learning from the Scene and Borrowing from the Rich: Tackling the Long Tail in Scene Graph Generation—0
Double Double Descent: On Generalization Errors in Transfer Learning between Linear Regression Tasks—0
Lifelong Learning using Eigentasks: Task Separation, Skill Acquisition and Selective Transfer—0
Importance Weighting with a Adversarial Network for Large-Scale Sleep Staging—0
Similarity-based transfer learning of decision policies—0
Mutual Information Based Knowledge Transfer Under State-Action Dimension MismatchCode0
UniT: Unified Knowledge Transfer for Any-shot Object Detection and Segmentation—0
What makes instance discrimination good for transfer learning?—0
Anti-Transfer Learning for Task Invariance in Convolutional Neural Networks for Speech ProcessingCode0
Deep Transfer Learning with Ridge Regression—0
Improving performance of CNN to predict likelihood of COVID-19 using chest X-ray images with preprocessing algorithms—0
Adversarial Training Based Multi-Source Unsupervised Domain Adaptation for Sentiment Analysis—0
Deep reinforcement learning for optical systems: A case study of mode-locked lasers—0
Bayesian Experience Reuse for Learning from Multiple Demonstrators—0
Transient Non-Stationarity and Generalisation in Deep Reinforcement Learning—0
Bombus Species Image Classification—0
A Review of Automated Diagnosis of COVID-19 Based on Scanning Images—0
Improving Cross-Lingual Transfer Learning for End-to-End Speech Recognition with Speech Translation—0
Multi-step Estimation for Gradient-based Meta-learning—0
Learning Constrained Dynamics with Gauss' Principle adhering Gaussian ProcessesCode0
Unsupervised Transfer Learning with Self-Supervised Remedy—0
Advance Warning Methodologies for COVID-19 using Chest X-Ray ImagesCode0
Efficient Architecture Search for Continual Learning—0
Knowledge-Based Learning through Feature Generation—0
Acoustic Anomaly Detection for Machine Sounds based on Image Transfer Learning—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