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 7701–7750 of 10307 papers

TitleStatusHype
Tool and Phase recognition using contextual CNN features—0
Tools for Extracting Spatio-Temporal Patterns in Meteorological Image Sequences: From Feature Engineering to Attention-Based Neural Networks—0
TOP-GAN: Label-Free Cancer Cell Classification Using Deep Learning with a Small Training Set—0
TopicBERT: A Transformer transfer learning based memory-graph approach for multimodal streaming social media topic detection—0
Topic-driven Distant Supervision Framework for Macro-level Discourse Parsing—0
TOPLight: Lightweight Neural Networks With Task-Oriented Pretraining for Visible-Infrared Recognition—0
TopoCL: Topological Contrastive Learning for Time Series—0
Topological derivative approach for deep neural network architecture adaptation—0
CTVR-EHO TDA-IPH Topological Optimized Convolutional Visual Recurrent Network for Brain Tumor Segmentation and Classification—0
Topological Vanilla Transfer Learning—0
Topology Change Aware Data-Driven Probabilistic Distribution State Estimation Based on Gaussian Process—0
To Supervise or Not to Supervise: Understanding and Addressing the Key Challenges of Point Cloud Transfer Learning—0
Total-Body Low-Dose CT Image Denoising using Prior Knowledge Transfer Technique with Contrastive Regularization Mechanism—0
To Transfer or Not to Transfer: Misclassification Attacks Against Transfer Learned Text Classifiers—0
To transfer or not transfer: Unified transferability metric and analysis—0
To Tune or Not to Tune? Adapting Pretrained Representations to Diverse Tasks—0
Toward a Geometrical Understanding of Self-supervised Contrastive Learning—0
Toward Co-creative Dungeon Generation via Transfer Learning—0
Toward Data-Driven Tutorial Question Answering with Deep Learning Conversational Models—0
Toward Edge-Efficient Dense Predictions with Synergistic Multi-Task Neural Architecture Search—0
Toward Educator-focused Automated Scoring Systems for Reading and Writing—0
Toward efficient resource utilization at edge nodes in federated learning—0
Toward Efficient Transfer Learning in 6G—0
Toward Fault Detection in Industrial Welding Processes with Deep Learning and Data Augmentation—0
Toward Improved Generalization: Meta Transfer of Self-supervised Knowledge on Graphs—0
Toward Drug-Target Interaction Prediction via Ensemble Modeling and Transfer Learning—0
Towards 3D Scene Understanding by Referring Synthetic Models—0
Towards Accurate Knowledge Transfer via Target-awareness Representation Disentanglement—0
Towards Accurate Quantization and Pruning via Data-free Knowledge Transfer—0
Towards a Cleaner Document-Oriented Multilingual Crawled Corpus—0
Towards Addressing Training Data Scarcity Challenge in Emerging Radio Access Networks: A Survey and Framework—0
Towards A Generalist Code Embedding Model Based On Massive Data Synthesis—0
Towards a General Model of Knowledge for Facial Analysis by Multi-Source Transfer Learning—0
Towards Agentic AI Networking in 6G: A Generative Foundation Model-as-Agent Approach—0
Towards a High-Performance Object Detector: Insights from Drone Detection Using ViT and CNN-based Deep Learning Models—0
Towards Algorithmic Fairness in Space-Time: Filling in Black Holes—0
Towards All-around Knowledge Transferring: Learning From Task-irrelevant Labels—0
Towards a Multi-Dataset for Complex Emotions Learning Based on Deep Neural Networks—0
Towards artificially intelligent recycling Improving image processing for waste classification—0
Towards a Unified View of Affinity-Based Knowledge Distillation—0
Towards a Universal Continuous Knowledge Base—0
Towards a Universal Vibration Analysis Dataset: A Framework for Transfer Learning in Predictive Maintenance and Structural Health Monitoring—0
Towards Automated COVID-19 Presence and Severity Classification—0
Towards Automated Melanoma Screening: Exploring Transfer Learning Schemes—0
Towards automatic generation of Piping and Instrumentation Diagrams (P&IDs) with Artificial Intelligence—0
Towards Automatic Lesion Classification in the Upper Aerodigestive Tract Using OCT and Deep Transfer Learning Methods—0
Towards Better Shale Gas Production Forecasting Using Transfer Learning—0
Towards building a Robust Industry-scale Question Answering System—0
Towards Compute-Optimal Transfer Learning—0
Towards Context-Aware Domain Generalization: Understanding the Benefits and Limits of Marginal Transfer Learning—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