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 7526–7550 of 10307 papers

TitleStatusHype
Explicit Alignment Objectives for Multilingual Bidirectional EncodersCode0
Interpretation of Swedish Sign Language using Convolutional Neural Networks and Transfer LearningCode0
Effects of the Nonlinearity in Activation Functions on the Performance of Deep Learning ModelsCode0
PP-LinkNet: Improving Semantic Segmentation of High Resolution Satellite Imagery with Multi-stage Training—0
Just Pick a Sign: Optimizing Deep Multitask Models with Gradient Sign DropoutCode0
Deep Ensembles for Low-Data Transfer Learning—0
Exploring the Uncertainty Properties of Neural Networks' Implicit Priors in the Infinite-Width Limit—0
Weight Squeezing: Reparameterization for Knowledge Transfer and Model Compression—0
Towards Accurate Quantization and Pruning via Data-free Knowledge Transfer—0
Will This Idea Spread Beyond Academia? Understanding Knowledge Transfer of Scientific Concepts across Text Corpora—0
SAR: Scale-Aware Restoration Learning for 3D Tumor Segmentation—0
Which Model to Transfer? Finding the Needle in the Growing Haystack—0
Model Selection for Cross-Lingual TransferCode0
Land Cover Semantic Segmentation Using ResUNet—0
Multilingual Argument Mining: Datasets and Analysis—0
Information-Theoretic Bounds on Transfer Generalization Gap Based on Jensen-Shannon Divergence—0
Asking Crowdworkers to Write Entailment Examples: The Best of Bad OptionsCode0
Collective Wisdom: Improving Low-resource Neural Machine Translation using Adaptive Knowledge Distillation—0
Multi-Stage Pre-training for Low-Resource Domain Adaptation—0
fairseq S2T: Fast Speech-to-Text Modeling with fairseqCode0
Multilingual Offensive Language Identification with Cross-lingual EmbeddingsCode0
Shape-aware Generative Adversarial Networks for Attribute Transfer—0
Multi-path Neural Networks for On-device Multi-domain Visual Classification—0
Be Your Own Best Competitor! Multi-Branched Adversarial Knowledge Transfer—0
HydroDeep -- A Knowledge Guided Deep Neural Network for Geo-Spatiotemporal Data Analysis—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