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 9851–9900 of 10307 papers

TitleStatusHype
Experience Selection Using Dynamics Similarity for Efficient Multi-Source Transfer Learning Between Robots—0
Experimental and causal view on information integration in autonomous agents—0
Experiments on transfer learning architectures for biomedical relation extraction—0
Expert-Free Online Transfer Learning in Multi-Agent Reinforcement Learning—0
Explainable AI based Glaucoma Detection using Transfer Learning and LIME—0
Explainable AI for Fair Sepsis Mortality Predictive Model—0
Explainable AI in Diagnosing and Anticipating Leukemia Using Transfer Learning Method—0
Explainable AI in Grassland Monitoring: Enhancing Model Performance and Domain Adaptability—0
Explainable AI in Handwriting Detection for Dyslexia Using Transfer Learning—0
Explainable COVID-19 Detection Using Chest CT Scans and Deep Learning—0
Explainable, Domain-Adaptive, and Federated Artificial Intelligence in Medicine—0
Explainable Lung Disease Classification from Chest X-Ray Images Utilizing Deep Learning and XAI—0
Explainable YOLO-Based Dyslexia Detection in Synthetic Handwriting Data—0
Explaining, Evaluating and Enhancing Neural Networks' Learned Representations—0
Explicit Connection Distillation—0
Explicit Domain Adaptation with Loosely Coupled Samples—0
Explicit Induction Bias for Transfer Learning with Convolutional Networks—0
Explicit Knowledge Transfer for Weakly-Supervised Code Generation—0
Exploit High-Dimensional RIS Information to Localization: What Is the Impact of Faulty Element?—0
Exploiting Both Domain-specific and Invariant Knowledge via a Win-win Transformer for Unsupervised Domain Adaptation—0
Exploiting CNNs for Semantic Segmentation with Pascal VOC—0
Exploiting Convolutional Representations for Multiscale Human Settlement Detection—0
Exploiting Convolution Filter Patterns for Transfer Learning—0
Exploiting Cross-Lingual Subword Similarities in Low-Resource Document Classification—0
Exploiting Low-Resource Code-Switching Data to Mandarin-English Speech Recognition Systems—0
Exploiting Multilingualism through Multistage Fine-Tuning for Low-Resource Neural Machine Translation—0
Exploiting Social Tags for Cross-Domain Collaborative Filtering—0
Exploiting the Logits: Joint Sign Language Recognition and Spell-Correction—0
Exploiting the Segment Anything Model (SAM) for Lung Segmentation in Chest X-ray Images—0
Exploiting Variational Domain-Invariant User Embedding for Partially Overlapped Cross Domain Recommendation—0
Exploiting Web Images for Weakly Supervised Object Detection—0
ExPLoRA: Parameter-Efficient Extended Pre-Training to Adapt Vision Transformers under Domain Shifts—0
Exploration de l'apprentissage par transfert pour l'analyse de textes des r\'eseaux sociaux (Exploring neural transfer learning for social media text analysis )—0
Exploration in Knowledge Transfer Utilizing Reinforcement Learning—0
Exploration of Adapter for Noise Robust Automatic Speech Recognition—0
Exploration of Dark Chemical Genomics Space via Portal Learning: Applied to Targeting the Undruggable Genome and COVID-19 Anti-Infective Polypharmacology—0
Exploration of Various Deep Learning Models for Increased Accuracy in Automatic Polyp Detection—0
Explorative Curriculum Learning for Strongly Correlated Electron Systems—0
Exploring bat song syllable representations in self-supervised audio encoders—0
Exploring Benefits of Transfer Learning in Neural Machine Translation—0
Exploring Bottom-up and Top-down Cues with Attentive Learning for Webly Supervised Object Detection—0
Exploring CausalWorld: Enhancing robotic manipulation via knowledge transfer and curriculum learning—0
Exploring connections of spectral analysis and transfer learning in medical imaging—0
Exploring Cross-Lingual Transfer Learning with Unsupervised Machine Translation—0
Exploring Cross-Lingual Transfer of Morphological Knowledge In Sequence-to-Sequence Models—0
Exploring Cultural Nuances in Emotion Perception Across 15 African Languages—0
Exploring Deep Neural Networks and Transfer Learning for Analyzing Emotions in Tweets—0
Exploring Fluent Query Reformulations with Text-to-Text Transformers and Reinforcement Learning—0
Exploring Image Transforms derived from Eye Gaze Variables for Progressive Autism Diagnosis—0
Exploring Knowledge Distillation of a Deep Neural Network for Multi-Script identification—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