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 1726–1750 of 10307 papers

TitleStatusHype
Contextualized Cross-Lingual Event Trigger Extraction with Minimal Resources—0
CG-CNN: Self-Supervised Feature Extraction Through Contextual Guidance and Transfer Learning—0
Continual Learning for Anomaly Detection in Surveillance Videos—0
Continual Lifelong Learning with Neural Networks: A Review—0
Adversarial Training Helps Transfer Learning via Better Representations—0
Approaching Neural Chinese Word Segmentation as a Low-Resource Machine Translation Task—0
Adversarial Training Based Multi-Source Unsupervised Domain Adaptation for Sentiment Analysis—0
Approaches for enhancing extrapolability in process-based and data-driven models in hydrology—0
Applying Transfer Learning To Deep Learned Models For EEG Analysis—0
Accelerating hydrodynamic simulations of urban drainage systems with physics-guided machine learning—0
Applying Transfer Learning for Improving Domain-Specific Search Experience Using Query to Question Similarity—0
Applying Knowledge Transfer for Water Body Segmentation in Peru—0
Adversarial Teacher-Student Learning for Unsupervised Domain Adaptation—0
Applying Deep Neural Networks to automate visual verification of manual bracket installations in aerospace—0
Applied Computer Vision on 2-Dimensional Lung X-Ray Images for Assisted Medical Diagnosis of Pneumonia—0
Active Learning Approaches to Enhancing Neural Machine Translation—0
Context-Aware Policy Reuse—0
Active flow control for three-dimensional cylinders through deep reinforcement learning—0
Adversarial-Robust Transfer Learning for Medical Imaging via Domain Assimilation—0
Content-Based Brain Tumor Retrieval for MR Images Using Transfer Learning—0
Application of Transfer Learning Approaches in Multimodal Wearable Human Activity Recognition—0
Application of Transfer Learning and Ensemble Learning in Image-level Classification for Breast Histopathology—0
Adversarial Robustness of Discriminative Self-Supervised Learning in Vision—0
Adversarial Network Compression—0
Application of Low-resource Machine Translation Techniques to Russian-Tatar Language Pair—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