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 8701–8750 of 10307 papers

TitleStatusHype
Computing with Categories in Machine Learning—0
Concept Drift Adaptation by Exploiting Historical Knowledge—0
Concept drift-tolerant transfer learning in dynamic environments.—0
Concept Formation and Alignment in Language Models: Bridging Statistical Patterns in Latent Space to Concept Taxonomy—0
Concept Transfer Learning for Adaptive Language Understanding—0
Conceptual Expansion Neural Architecture Search (CENAS)—0
Covariate-Elaborated Robust Partial Information Transfer with Conditional Spike-and-Slab Prior—0
CON: Continual Object Navigation via Data-Free Inter-Agent Knowledge Transfer in Unseen and Unfamiliar Places—0
Concrete Surface Crack Detection with Convolutional-based Deep Learning Models—0
Concurrent Discrimination and Alignment for Self-Supervised Feature Learning—0
Condensed Sample-Guided Model Inversion for Knowledge Distillation—0
Conditional Adapters: Parameter-efficient Transfer Learning with Fast Inference—0
Conditional Bures Metric for Domain Adaptation—0
Conditional computation in neural networks: principles and research trends—0
Conditional Data Synthesis Augmentation—0
Conditional Electrocardiogram Generation Using Hierarchical Variational Autoencoders—0
Conditional Neural Processes for Molecules—0
Conditional Loss and Deep Euler Scheme for Time Series Generation—0
Confidence Aware Neural Networks for Skin Cancer Detection—0
Confidence-Aware Subject-to-Subject Transfer Learning for Brain-Computer Interface—0
Confidence Estimation in Unsupervised Deep Change Vector Analysis—0
Confidence-Nets: A Step Towards better Prediction Intervals for regression Neural Networks on small datasets—0
Confidence Preserving Machine for Facial Action Unit Detection—0
Conformal Prediction Under Generalized Covariate Shift with Posterior Drift—0
Conjuring Positive Pairs for Efficient Unification of Representation Learning and Image Synthesis—0
Connecting the Dots between Audio and Text without Parallel Data through Visual Knowledge Transfer—0
Consensus-Based Transfer Linear Support Vector Machines for Decentralized Multi-Task Multi-Agent Learning—0
Conservation AI: Live Stream Analysis for the Detection of Endangered Species Using Convolutional Neural Networks and Drone Technology—0
Considerations for a PAP Smear Image Analysis System with CNN Features—0
Considering Race a Problem of Transfer Learning—0
Consistency and Diversity induced Human Motion Segmentation—0
Constrained Deep Transfer Feature Learning and its Applications—0
Constraining Latent Space to Improve Deep Self-Supervised e-Commerce Products Embeddings for Downstream Tasks—0
Constructive and Toxic Speech Detection for Open-domain Social Media Comments in Vietnamese—0
Contact Area Detector using Cross View Projection Consistency for COVID-19 Projects—0
Content-Based Brain Tumor Retrieval for MR Images Using Transfer Learning—0
Context-aware Domain Adaptation for Time Series Anomaly Detection—0
Context-Aware Policy Reuse—0
Context-Aware Text Normalisation for Historical Dialects—0
Context-driven Visual Object Recognition based on Knowledge Graphs—0
Context-PEFT: Efficient Multi-Modal, Multi-Task Fine-Tuning—0
Contextualized Attention-based Knowledge Transfer for Spoken Conversational Question Answering—0
Contextualized Cross-Lingual Event Trigger Extraction with Minimal Resources—0
CG-CNN: Self-Supervised Feature Extraction Through Contextual Guidance and Transfer Learning—0
Contextual Transformation Networks for Online Continual Learning—0
Relational Modeling for Robust and Efficient Pulmonary Lobe Segmentation in CT Scans—0
Continual Few-shot Intent Detection—0
Continual Learning for Anomaly Detection in Surveillance Videos—0
Continual Learning for Tumor Classification in Histopathology Images—0
Continual Learning in the Presence of Spurious Correlation—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