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 8976–9000 of 10307 papers

TitleStatusHype
Transferring Knowledge Fragments for Learning Distance Metric from A Heterogeneous Domain—0
On The Power of Curriculum Learning in Training Deep NetworksCode0
The Information Complexity of Learning Tasks, their Structure and their Distance—0
Biometric Fish Classification of Temperate Species Using Convolutional Neural Network with Squeeze-and-Excitation—0
Meta-Learning Acquisition Functions for Transfer Learning in Bayesian OptimizationCode0
Resource Efficient 3D Convolutional Neural NetworksCode0
Active Transfer Learning Network: A Unified Deep Joint Spectral-Spatial Feature Learning Model For Hyperspectral Image Classification—0
A Many Objective Optimization Approach for Transfer Learning in EEG Classification—0
Learning Implicit Generative Models by Matching Perceptual Features—0
Transfer Learning with Sparse Associative Memories—0
Transfer Learning for Performance Modeling of Deep Neural Network SystemsCode0
Cross-lingual transfer learning for spoken language understanding—0
Stacked Semantic-Guided Network for Zero-Shot Sketch-Based Image Retrieval—0
Understanding the efficacy, reliability and resiliency of computer vision techniques for malware detection and future research directions—0
Correlation Congruence for Knowledge DistillationCode0
Identifying disease-free chest X-ray images with deep transfer learning—0
Lautum Regularization for Semi-supervised Transfer Learning—0
Easy Transfer Learning By Exploiting Intra-domain Structures—0
Unveiling phase transitions with machine learningCode0
Transfer Learning for Clinical Time Series Analysis using Deep Neural Networks—0
Creativity Inspired Zero-Shot LearningCode0
Multitask Soft Option LearningCode0
Does an LSTM forget more than a CNN? An empirical study of catastrophic forgetting in NLP—0
Local Aggregation for Unsupervised Learning of Visual EmbeddingsCode0
Cross-Subject Transfer Learning in Human Activity Recognition Systems using Generative Adversarial Networks—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