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 9226–9250 of 10307 papers

TitleStatusHype
SynEVO: A neuro-inspired spatiotemporal evolutional framework for cross-domain adaptation—0
3D Printed Brain-Controlled Robot-Arm Prosthetic via Embedded Deep Learning from sEMG Sensors—0
Boosting Automatic COVID-19 Detection Performance with Self-Supervised Learning and Batch Knowledge Ensembling—0
CoLI-Machine Learning Approaches for Code-mixed Language Identification at the Word Level in Kannada-English Texts—0
Collaborative Adversarial Learning for RelationalLearning on Multiple Bipartite Graphs—0
An Improved Transfer Model: Randomized Transferable Machine—0
Collaborative Group Learning—0
Collaborative Knowledge Fusion: A Novel Approach for Multi-task Recommender Systems via LLMs—0
Learning with Shared Representations: Statistical Rates and Efficient Algorithms—0
Collaborative Memory: Multi-User Memory Sharing in LLM Agents with Dynamic Access Control—0
Collaborative Pressure Ulcer Prevention: An Automated Skin Damage and Pressure Ulcer Assessment Tool for Nursing Professionals, Patients, Family Members and Carers—0
Collaborative Recommendation with Auxiliary Data: A Transfer Learning View—0
Collaborative Teacher-Student Learning via Multiple Knowledge Transfer—0
Randomize to Generalize: Domain Randomization for Runway FOD Detection—0
Boosted Zero-Shot Learning with Semantic Correlation Regularization—0
Bone Marrow Cytomorphology Cell Detection using InceptionResNetV2—0
Collective Knowledge Graph Completion with Mutual Knowledge Distillation—0
Collective Wisdom: Improving Low-resource Neural Machine Translation using Adaptive Knowledge Distillation—0
Collusion Detection with Graph Neural Networks—0
Colorectal cancer diagnosis from histology images: A comparative study—0
Color Enhancement for V-PCC Compressed Point Cloud via 2D Attribute Map Optimization—0
Bombus Species Image Classification—0
Combating Uncertainty with Novel Losses for Automatic Left Atrium Segmentation—0
Combination of Domain Knowledge and Deep Learning for Sentiment Analysis of Short and Informal Messages on Social Media—0
Combination of multiple neural networks using transfer learning and extensive geometric data augmentation for assessing cellularity scores in histopathology images—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