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 6451–6475 of 10307 papers

TitleStatusHype
Identification of Social-Media Platform of Videos through the Use of Shared Features—0
Powering Comparative Classification with Sentiment Analysis via Domain Adaptive Knowledge TransferCode0
Naturalness Evaluation of Natural Language Generation in Task-oriented Dialogues using BERT—0
CRNNTL: convolutional recurrent neural network and transfer learning for QSAR modelling—0
FDA: Feature Decomposition and Aggregation for Robust Airway Segmentation—0
Contrastive Learning with Temporal Correlated Medical Images: A Case Study using Lung Segmentation in Chest X-RaysCode0
GPT-3 Models are Poor Few-Shot Learners in the Biomedical DomainCode0
Deep SIMBAD: Active Landmark-based Self-localization Using Ranking -based Scene Descriptor—0
External knowledge transfer deployment inside a simple double agent Viterbi algorithm—0
Training Deep Networks from Zero to Hero: avoiding pitfalls and going beyondCode0
FBDNN: Filter Banks and Deep Neural Networks for Portable and Fast Brain-Computer InterfacesCode0
Robust Importance Sampling for Error Estimation in the Context of Optimal Bayesian Transfer Learning—0
Automatic Online Multi-Source Domain AdaptationCode0
A Bayesian Approach to (Online) Transfer Learning: Theory and Algorithms—0
CAM-loss: Towards Learning Spatially Discriminative Feature Representations—0
Transfer of Pretrained Model Weights Substantially Improves Semi-Supervised Image ClassificationCode0
Coarse-To-Fine And Cross-Lingual ASR Transfer—0
Adapted End-to-End Coreference Resolution System for Anaphoric Identities in Dialogues—0
InFoBERT: Zero-Shot Approach to Natural Language Understanding Using Contextualized Word Embedding—0
Cross-lingual Fine-tuning for Abstractive Arabic Text Summarization—0
BERT-PersNER: A New Model for Persian Named Entity Recognition—0
OptAGAN: Entropy-based finetuning on text VAE-GANCode0
Using Transfer Learning to Automatically Mark L2 Writing Texts—0
Transfer Learning for Czech Historical Named Entity Recognition—0
Using convolutional neural networks for the classification of breast cancer imagesCode0
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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