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 3776–3800 of 10307 papers

TitleStatusHype
Exploring Knowledge Transfer in Evolutionary Many-task Optimization: A Complex Network Perspective—0
Coefficient Shape Transfer Learning for Functional Linear Regression—0
A Survey of Fish Tracking Techniques Based on Computer Vision—0
Coevo: a collaborative design platform with artificial agents—0
Exploring Multi-Level Threats in Telegram Data with AI-Human Annotation: A Preliminary Study—0
A Review of Deep Learning Approaches for Non-Invasive Cognitive Impairment Detection—0
Exploring Multimodal Features and Fusion Strategies for Analyzing Disaster Tweets—0
Deep 3D-Zoom Net: Unsupervised Learning of Photo-Realistic 3D-Zoom—0
Exploring Named Entity Recognition As an Auxiliary Task for Slot Filling in Conversational Language Understanding—0
Deep 3D Face Identification—0
Exploring Non-Verbal Predicates in Semantic Role Labeling: Challenges and Opportunities—0
A review of sentiment analysis research in Arabic language—0
A Review of Deep Transfer Learning and Recent Advancements—0
A Multimodal Lightweight Approach to Fault Diagnosis of Induction Motors in High-Dimensional Dataset—0
Cognitive Learning-Aided Multi-Antenna Communications—0
FedTune: A Deep Dive into Efficient Federated Fine-Tuning with Pre-trained Transformers—0
Cognitive simulation models for inertial confinement fusion: Combining simulation and experimental data—0
Exploring speech style spaces with language models: Emotional TTS without emotion labels—0
A review of technical factors to consider when designing neural networks for semantic segmentation of Earth Observation imagery—0
DEEGITS: Deep Learning based Framework for Measuring Heterogenous Traffic State in Challenging Traffic Scenarios—0
Exploring Task Unification in Graph Representation Learning via Generative Approach—0
Coherent and Consistent Relational Transfer Learning with Autoencoders—0
Automated Visual Attention Detection using Mobile Eye Tracking in Behavioral Classroom Studies—0
Automated Tomato Maturity Estimation Using an Optimized Residual Model with Pruning and Quantization Techniques—0
Decouple Non-parametric Knowledge Distillation For End-to-end Speech Translation—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