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 6626–6650 of 10307 papers

TitleStatusHype
ISTRBoost: Importance Sampling Transfer Regression using Boosting—0
AfriWOZ: Corpus for Exploiting Cross-Lingual Transferability for Generation of Dialogues in Low-Resource, African Languages—0
Iterative Auto-Annotation for Scientific Named Entity Recognition Using BERT-Based Models—0
Iterative Dual Domain Adaptation for Neural Machine Translation—0
Iteratively Pruned Deep Learning Ensembles for COVID-19 Detection in Chest X-rays—0
Iterative Multi-domain Regularized Deep Learning for Anatomical Structure Detection and Segmentation from Ultrasound Images—0
Iterative self-transfer learning: A general methodology for response time-history prediction based on small dataset—0
IT-RUDA: Information Theory Assisted Robust Unsupervised Domain Adaptation—0
Sentiment Relevance—0
It's Not About the Journey; It's About the Destination: Following Soft Paths Under Question-Guidance for Visual Reasoning—0
A Comprehensive Survey of Deep Transfer Learning for Anomaly Detection in Industrial Time Series: Methods, Applications, and Directions—0
Japanese Zero Anaphora Resolution Can Benefit from Parallel Texts Through Neural Transfer Learning—0
AOSLO-net: A deep learning-based method for automatic segmentation of retinal microaneurysms from adaptive optics scanning laser ophthalmoscope images—0
AnyTouch: Learning Unified Static-Dynamic Representation across Multiple Visuo-tactile Sensors—0
JiraiBench: A Bilingual Benchmark for Evaluating Large Language Models' Detection of Human Self-Destructive Behavior Content in Jirai Community—0
基于知识迁移的情感-原因对抽取(Emotion-Cause Pair Extraction Based on Knowledge-Transfer)—0
Joint autoencoders: a flexible meta-learning framework—0
Joint auto-encoders: a flexible multi-task learning framework—0
Beamforming and Resource Allocation for Delay Optimization in RIS-Assisted OFDM Systems—0
Joint Deep Cross-Domain Transfer Learning for Emotion Recognition—0
Joint Detection of Malicious Domains and Infected Clients—0
Joint Emotion Analysis via Multi-task Gaussian Processes—0
Joint Identifiability of Cross-Domain Recommendation via Hierarchical Subspace Disentanglement—0
Joint Liver Lesion Segmentation and Classification via Transfer Learning—0
Any-Shot Sequential Anomaly Detection in Surveillance Videos—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