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 1801–1825 of 10307 papers

TitleStatusHype
Classification of Industrial Control Systems screenshots using Transfer Learning—0
Application of Artificial Intelligence in the Classification of Microscopical Starch Images for Drug Formulation—0
Classification of Luminal Subtypes in Full Mammogram Images Using Transfer Learning—0
Classification of Melanocytic Nevus Images using BigTransfer (BiT)—0
Classification of Microscopy Images of Breast Tissue: Region Duplication based Self-Supervision vs. Off-the Shelf Deep Representations—0
A Preliminary Study on Environmental Sound Classification Leveraging Large-Scale Pretrained Model and Semi-Supervised Learning—0
Classification of Skin Cancer Images using Convolutional Neural Networks—0
Classification of Skin Disease Using Transfer Learning in Convolutional Neural Networks—0
CIAO! A Contrastive Adaptation Mechanism for Non-Universal Facial Expression Recognition—0
Classifying Judgements using Transfer Learning—0
A Primer on Pretrained Multilingual Language Models—0
Adversarial Multi-Source Transfer Learning in Healthcare: Application to Glucose Prediction for Diabetic People—0
Class Relationship Embedded Learning for Source-Free Unsupervised Domain Adaptation—0
Classroom-Inspired Multi-Mentor Distillation with Adaptive Learning Strategies—0
Class Similarity-Based Multimodal Classification under Heterogeneous Category Sets—0
Class-Specific Channel Attention for Few-Shot Learning—0
Class-Specific Data Augmentation: Bridging the Imbalance in Multiclass Breast Cancer Classification—0
Class Subset Selection for Transfer Learning using Submodularity—0
CLCE: An Approach to Refining Cross-Entropy and Contrastive Learning for Optimized Learning Fusion—0
Cleaning tasks knowledge transfer between heterogeneous robots: a deep learning approach—0
Apple Leaf Disease Identification through Region-of-Interest-Aware Deep Convolutional Neural Network—0
CLEAR: Cumulative LEARning for One-Shot One-Class Image Recognition—0
Contrastive Representation Distillation via Multi-Scale Feature Decoupling—0
Adversarial Multi-Agent Reinforcement Learning for Proactive False Data Injection Detection—0
Active Adversarial Domain Adaptation—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