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 8376–8400 of 10307 papers

TitleStatusHype
Inspect Transfer Learning Architecture with Dilated Convolution—0
Transfer Learning Toolkit: Primers and BenchmarksCode0
Eliminating artefacts in Polarimetric Images using Deep LearningCode0
Efficient Hardware Implementation of Incremental Learning and Inference on Chip—0
Commit2Vec: Learning Distributed Representations of Code Changes—0
Unsupervised Representation Learning by Discovering Reliable Image Relations—0
Walking the Tightrope: An Investigation of the Convolutional Autoencoder BottleneckCode0
Towards Making Deep Transfer Learning Never Hurt—0
Transfer Learning of fMRI Dynamics—0
Glyph: Fast and Accurately Training Deep Neural Networks on Encrypted Data—0
Liver Steatosis Segmentation with Deep Learning Methods—0
QC-Automator: Deep Learning-based Automated Quality Control for Diffusion MR Images—0
Deep Discriminative Fine-Tuning for Cancer Type Classification—0
Deep Learning for Over-the-Air Non-Orthogonal Signal Classification—0
BiNet: Degraded-Manuscript Binarization in Diverse Document Textures and Layouts using Deep Encoder-Decoder Networks—0
AMPL: A Data-Driven Modeling Pipeline for Drug DiscoveryCode0
A Smartphone-Based Skin Disease Classification Using MobileNet CNN—0
Random Projections of Mel-Spectrograms as Low-Level Features for Automatic Music Genre Classification—0
Data Efficient Direct Speech-to-Text Translation with Modality Agnostic Meta-Learning—0
NegBERT: A Transfer Learning Approach for Negation Detection and Scope ResolutionCode0
Transfer Value Iteration Networks—0
Missing Features Reconstruction and Its Impact on Classification Accuracy—0
On Architectures for Including Visual Information in Neural Language Models for Image DescriptionCode0
How Language-Neutral is Multilingual BERT?Code0
Deep Transfer Learning for Thermal Dynamics Modeling in Smart Buildings—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