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 8301–8325 of 10307 papers

TitleStatusHype
Online Bagging for Anytime Transfer Learning—0
A Framework for Fast Scalable BNN Inference using Googlenet and Transfer Learning—0
Online Electric Vehicle Charging Detection Based on Memory-based Transformer using Smart Meter Data—0
Online Few-shot Gesture Learning on a Neuromorphic Processor—0
Online Gesture Recognition using Transformer and Natural Language Processing—0
A foundational neural operator that continuously learns without forgetting—0
A Foreground Inference Network for Video Surveillance Using Multi-View Receptive Field—0
The Fast and Accurate Approach to Detection and Segmentation of Melanoma Skin Cancer using Fine-tuned Yolov3 and SegNet Based on Deep Transfer Learning—0
Online Learning for Recommendations at Grubhub—0
Online Learning with Radial Basis Function Networks—0
Stock and market index prediction using Informer network—0
Online Multi-Source Domain Adaptation through Gaussian Mixtures and Dataset Dictionary Learning—0
Online Policy Distillation with Decision-Attention—0
Online Sensor Hallucination via Knowledge Distillation for Multimodal Image Classification—0
Online Transfer Learning for RSV Case Detection—0
Online Transfer Learning in Reinforcement Learning Domains—0
Online Transfer Learning: Negative Transfer and Effect of Prior Knowledge—0
Online Video Super-Resolution with Convolutional Kernel Bypass Graft—0
On mechanisms for transfer using landmark value functions in multi-task lifelong reinforcement learning—0
On Neural Consolidation for Transfer in Reinforcement Learning—0
On partitioning of an SHM problem and parallels with transfer learning—0
On Plasticity, Invariance, and Mutually Frozen Weights in Sequential Task Learning—0
On-ramp and Off-ramp Traffic Flows Estimation Based on A Data-driven Transfer Learning Framework—0
On Reinforcement Learning for Full-length Game of StarCraft—0
A Food Photography App with Image Recognition for Thai Food—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