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 7376–7400 of 10307 papers

TitleStatusHype
Towards All-around Knowledge Transferring: Learning From Task-irrelevant Labels—0
A Transfer Learning Based Active Learning Framework for Brain Tumor Classification—0
On the Effectiveness of Vision Transformers for Zero-shot Face Anti-SpoofingCode0
In-Car driver response classification using Deep Learning (CNN) based Computer VisionCode0
Statistical Inference for Maximin Effects: Identifying Stable Associations across Multiple Studies—0
Transfer learning of chaotic systems—0
Query-based Targeted Action-Space Adversarial Policies on Deep Reinforcement Learning AgentsCode0
Robotic self-representation improves manipulation skills and transfer learning—0
A Study of Domain Generalization on Ultrasound-based Multi-Class Segmentation of Arteries, Veins, Ligaments, and Nerves Using Transfer Learning—0
On the Transferability of VAE Embeddings using Relational Knowledge with Semi-Supervision—0
Multi-Modal Emotion Detection with Transfer Learning—0
Roof fall hazard detection with convolutional neural networks using transfer learningCode0
A Transfer Learning Framework for Anomaly Detection Using Model of Normality—0
Improving Model Accuracy for Imbalanced Image Classification Tasks by Adding a Final Batch Normalization Layer: An Empirical Study—0
Using IPA-Based Tacotron for Data Efficient Cross-Lingual Speaker Adaptation and Pronunciation Enhancement—0
Transferred Fusion Learning using Skipped Networks—0
Real-Time Decentralized knowledge Transfer at the EdgeCode0
Interpretable and synergistic deep learning for visual explanation and statistical estimations of segmentation of disease features from medical images—0
Robust and flexible learning of a high-dimensional classification rule using auxiliary outcomes—0
Recognizing More Emotions with Less Data Using Self-supervised Transfer Learning—0
Learning from THEODORE: A Synthetic Omnidirectional Top-View Indoor Dataset for Deep Transfer Learning—0
Filtered Manifold Alignment—0
Classification of COVID-19 in Chest CT Images using Convolutional Support Vector Machines—0
Generic Semi-Supervised Adversarial Subject Translation for Sensor-Based Human Activity Recognition—0
An ensemble-based approach by fine-tuning the deep transfer learning models to classify pneumonia from chest X-ray 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