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 2976–3000 of 10307 papers

TitleStatusHype
A vision transformer-based framework for knowledge transfer from multi-modal to mono-modal lymphoma subtyping models—0
EEG-based Cognitive Load Classification using Feature Masked Autoencoding and Emotion Transfer Learning—0
Understanding Activation Patterns in Artificial Neural Networks by Exploring Stochastic Processes—0
Pixel to policy: DQN Encoders for within & cross-game reinforcement learning—0
LP-MusicCaps: LLM-Based Pseudo Music CaptioningCode2
Cardiac MRI Orientation Recognition and Standardization using Deep Neural NetworksCode0
UDAMA: Unsupervised Domain Adaptation through Multi-discriminator Adversarial Training with Noisy Labels Improves Cardio-fitness PredictionCode0
Utilisation of open intent recognition models for customer support intent detection—0
Hybrid quantum transfer learning for crack image classification on NISQ hardware—0
Structural Transfer Learning in NL-to-Bash Semantic Parsers—0
Sampling to Distill: Knowledge Transfer from Open-World Data—0
Stylized Projected GAN: A Novel Architecture for Fast and Realistic Image Generation—0
Gastrointestinal Mucosal Problems Classification with Deep Learning—0
Count, Decode and Fetch: A New Approach to Handwritten Chinese Character Error Correction—0
Dimensionless Policies based on the Buckingham π Theorem: Is This a Good Way to Generalize Numerical Results?Code0
Cross-dimensional transfer learning in medical image segmentation with deep learningCode0
Evaluating the structure of cognitive tasks with transfer learning—0
Point Clouds Are Specialized Images: A Knowledge Transfer Approach for 3D Understanding—0
Fluorescent Neuronal Cells v2: Multi-Task, Multi-Format Annotations for Deep Learning in Microscopy—0
Towards Generalist Biomedical AI—0
Reinforcement Learning by Guided Safe Exploration—0
Transfer Learning for Portfolio Optimization—0
ChildGAN: Large Scale Synthetic Child Facial Data Using Domain Adaptation in StyleGAN—0
Spectral-DP: Differentially Private Deep Learning through Spectral Perturbation and Filtering—0
Re-mine, Learn and Reason: Exploring the Cross-modal Semantic Correlations for Language-guided HOI detection—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