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 4326–4350 of 10307 papers

TitleStatusHype
Audio-Visual Scene Classification Using A Transfer Learning Based Joint Optimization Strategy—0
A Methodology for Controlling the Emotional Expressiveness in Synthetic Speech -- a Deep Learning approach—0
Audio-visual scene classification: analysis of DCASE 2021 Challenge submissions—0
Audio-visual cross-modality knowledge transfer for machine learning-based in-situ monitoring in laser additive manufacturing—0
CtlGAN: Few-shot Artistic Portraits Generation with Contrastive Transfer Learning—0
CT Image Enhancement Using Stacked Generative Adversarial Networks and Transfer Learning for Lesion Segmentation Improvement—0
A Method of Augmenting Bilingual Terminology by Taking Advantage of the Conceptual Systematicity of Terminologies—0
Hierarchical Continual Reinforcement Learning via Large Language Model—0
Hidden Markov tree models for semantic class induction—0
CST: Calibration Side-Tuning for Parameter and Memory Efficient Transfer Learning—0
A Method for Building a Commonsense Inference Dataset based on Basic Events—0
CSG0: Continual Urban Scene Generation with Zero Forgetting—0
CSECU-DSG at WNUT-2020 Task 2: Exploiting Ensemble of Transfer Learning and Hand-crafted Features for Identification of Informative COVID-19 English Tweets—0
Adaptive multi-channel event segmentation and feature extraction for monitoring health outcomes—0
CrystalGPT: Enhancing system-to-system transferability in crystallization prediction and control using time-series-transformers—0
Crude Oil-related Events Extraction and Processing: A Transfer Learning Approach—0
CrowdTransfer: Enabling Crowd Knowledge Transfer in AIoT Community—0
Crowd Sourcing based Active Learning Approach for Parking Sign Recognition—0
A Two-Stage Approach towards Generalization in Knowledge Base Question Answering—0
A Meta-transfer Learning framework for Visually Grounded Compositional Concept Learning—0
Hey AI Can You Grade My Essay?: Automatic Essay Grading—0
Crowd-Powered Data Mining—0
CrosswalkNet: An Optimized Deep Learning Framework for Pedestrian Crosswalk Detection in Aerial Images with High-Performance Computing—0
A Two-Stage Approach towards Generalization in Knowledge Base Question Answering—0
CrossVoice: Crosslingual Prosody Preserving Cascade-S2ST using Transfer Learning—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