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 7851–7900 of 10307 papers

TitleStatusHype
Transfer Learning of Semantic Segmentation Methods for Identifying Buried Archaeological Structures on LiDAR Data—0
TransBO: Hyperparameter Optimization via Two-Phase Transfer Learning—0
Transcending Controlled Environments Assessing the Transferability of ASRRobust NLU Models to Real-World Applications—0
Transductive Adaptation of Black Box Predictions—0
Transductive Label Augmentation for Improved Deep Network Learning—0
Transductive Multi-class and Multi-label Zero-shot Learning—0
Transductive Multi-view Zero-Shot Learning—0
Transductive Zero-Shot Hashing via Coarse-to-Fine Similarity Mining—0
Transductive Zero-Shot Learning with a Self-training dictionary approach—0
Transferability analysis of data-driven additive manufacturing knowledge: a case study between powder bed fusion and directed energy deposition—0
Transferability Estimation Based On Principal Gradient Expectation—0
Transferability Estimation for Semantic Segmentation Task—0
Transferability Estimation using Bhattacharyya Class Separability—0
Transferability-Guided Cross-Domain Cross-Task Transfer Learning—0
Transferability in Deep Learning: A Survey—0
Unsupervised Personalization of an Emotion Recognition System: The Unique Properties of the Externalization of Valence in Speech—0
Unsupervised Person Re-identification via Multi-label Classification—0
Unsupervised Random Walk Sentence Embeddings: A Strong but Simple Baseline—0
Unsupervised Representation Learning by Invariance Propagation—0
Unsupervised Representation Learning by Discovering Reliable Image Relations—0
Unsupervised Representation Learning with Future Observation Prediction for Speech Emotion Recognition—0
Unsupervised Representations Improve Supervised Learning in Speech Emotion Recognition—0
Unsupervised Representation Transfer for Small Networks: I Believe I Can Distill On-the-Fly—0
Unsupervised state representation learning with robotic priors: a robustness benchmark—0
Unsupervised Transfer Learning in Multilingual Neural Machine Translation with Cross-Lingual Word Embeddings—0
Unsupervised Transfer Learning via Adversarial Contrastive Training—0
Unsupervised Transfer Learning via BERT Neuron Selection—0
Unsupervised Transfer Learning with Self-Supervised Remedy—0
Unsupervised Word Translation Pairing using Refinement based Point Set Registration—0
Unsupervised Word Translation with Adversarial Autoencoder—0
Untangling the Influence of Typology, Data and Model Architecture on Ranking Transfer Languages for Cross-Lingual POS Tagging—0
Untargeted Code Authorship Evasion with Seq2Seq Transformation—0
Unveiling the Potential of Deep Learning Models for Solar Flare Prediction in Near-Limb Regions—0
UoB\_UK at SemEval 2021 Task 2: Zero-Shot and Few-Shot Learning for Multi-lingual and Cross-lingual Word Sense Disambiguation.—0
UofA-Truth at Factify 2022 : Transformer And Transfer Learning Based Multi-Modal Fact-Checking—0
UofL at SemEval-2016 Task 4: Multi Domain word2vec for Twitter Sentiment Classification—0
UParse: the Edinburgh system for the CoNLL 2017 UD shared task—0
Urban flows prediction from spatial-temporal data using machine learning: A survey—0
UrbanSARFloods: Sentinel-1 SLC-Based Benchmark Dataset for Urban and Open-Area Flood Mapping—0
Urban traffic analysis and forecasting through shared Koopman eigenmodes—0
Use of Combined Topic Models in Unsupervised Domain Adaptation for Word Sense Disambiguation—0
Use of Deep Neural Networks for Uncertain Stress Functions with Extensions to Impact Mechanics—0
Use of Multifidelity Training Data and Transfer Learning for Efficient Construction of Subsurface Flow Surrogate Models—0
Use of Transfer Learning and Wavelet Transform for Breast Cancer Detection—0
User lung cancer classification using efficientnet from ct scan images—0
User-specific Adaptive Fine-tuning for Cross-domain Recommendations—0
Using Artificial Intelligence for the Automation of Knitting Patterns—0
Using autoencoders and deep transfer learning to determine the stellar parameters of 286 CARMENES M dwarfs—0
Using Deep Networks and Transfer Learning to Address Disinformation—0
Towards Using Diachronic Distributed Word Representations as Models of Lexical Development—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