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 3676–3700 of 10307 papers

TitleStatusHype
Why Is Public Pretraining Necessary for Private Model Training?—0
Multilingual Content Moderation: A Case Study on RedditCode0
A Comprehensive Evaluation Study on Risk Level Classification of Melanoma by Computer Vision on ISIC 2016-2020 Datasets—0
Generative Causal Representation Learning for Out-of-Distribution Motion ForecastingCode0
sMRI-PatchNet: A novel explainable patch-based deep learning network for Alzheimer's disease diagnosis and discriminative atrophy localisation with Structural MRI—0
Adversarial Contrastive Distillation with Adaptive Denoising—0
Self-Supervised Representation Learning from Temporal Ordering of Automated Driving Sequences—0
Deep Implicit Distribution Alignment Networks for Cross-Corpus Speech Emotion Recognition—0
Deep comparisons of Neural Networks from the EEGNet familyCode1
Cross-Domain Label Propagation for Domain Adaptation with Discriminative Graph Self-Learning—0
Distributed Learning in Heterogeneous Environment: federated learning with adaptive aggregation and computation reduction—0
Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image ClassificationCode1
FOSI: Hybrid First and Second Order OptimizationCode0
Frugal day-ahead forecasting of multiple local electricity loads by aggregating adaptive models—0
Learning to diagnose cirrhosis from radiological and histological labels with joint self and weakly-supervised pretraining strategies—0
Revisiting Hidden Representations in Transfer Learning for Medical ImagingCode0
Towards Efficient Visual Adaption via Structural Re-parameterizationCode1
A Survey of Geometric Optimization for Deep Learning: From Euclidean Space to Riemannian Manifold—0
A Meta-Learning Approach to Population-Based Modelling of Structures—0
Cliff-Learning—0
Intelligent Model Update Strategy for Sequential Recommendation—0
Detection and classification of vocal productions in large scale audio recordingsCode0
Graph schemas as abstractions for transfer learning, inference, and planning—0
Gradient-Based Automated Iterative Recovery for Parameter-Efficient Tuning—0
Knowledge from Large-Scale Protein Contact Prediction Models Can Be Transferred to the Data-Scarce RNA Contact Prediction TaskCode0
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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