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 8051–8100 of 10307 papers

TitleStatusHype
Assistive Completion of Agrammatic Aphasic Sentences: A Transfer Learning Approach using Neurolinguistics-based Synthetic Dataset—0
Assistive Diagnostic Tool for Brain Tumor Detection using Computer Vision—0
Associative embeddings for large-scale knowledge transfer with self-assessment—0
A transfer learning framework for weak-to-strong generalization—0
A Statistical Theory of Regularization-Based Continual Learning—0
A Study of Domain Generalization on Ultrasound-based Multi-Class Segmentation of Arteries, Veins, Ligaments, and Nerves Using Transfer Learning—0
A Study of the Generalizability of Self-Supervised Representations—0
A Study of the Tasks and Models in Machine Reading Comprehension—0
A Study of Transfer Learning in Music Source Separation—0
A Study on Bootstrapping Bilingual Vector Spaces from Non-Parallel Data (and Nothing Else)—0
A study on Deep Convolutional Neural Networks, Transfer Learning and Ensemble Model for Breast Cancer Detection—0
A Study on Representation Transfer for Few-Shot Learning—0
A Study on Robustness to Perturbations for Representations of Environmental Sound—0
A study on the plasticity of neural networks—0
A Study on Using Different Audio Lengths in Transfer Learning for Improving Chainsaw Sound Recognition—0
A Study on Using Transfer Learning to Improve BERT Model for Emotional Classification of Chinese Lyrics—0
A Survey of Deep Visual Cross-Domain Few-Shot Learning—0
A Survey of Geometric Optimization for Deep Learning: From Euclidean Space to Riemannian Manifold—0
A Survey of GPT-3 Family Large Language Models Including ChatGPT and GPT-4—0
A Survey of IMU Based Cross-Modal Transfer Learning in Human Activity Recognition—0
A Survey of Inductive Biases for Factorial Representation-Learning—0
A Survey of Large Language Models for Financial Applications: Progress, Prospects and Challenges—0
A Survey of Latent Factor Models in Recommender Systems—0
A Survey of Learning on Small Data: Generalization, Optimization, and Challenge—0
A Survey of Methods to Leverage Monolingual Data in Low-resource Neural Machine Translation—0
A Survey of Multilingual Models for Automatic Speech Recognition—0
A Brief Survey of Multilingual Neural Machine Translation—0
A Survey of Reinforcement Learning for Optimization in Automation—0
A Survey of Surface Defect Detection of Industrial Products Based on A Small Number of Labeled Data—0
A Survey of the Impact of Self-Supervised Pretraining for Diagnostic Tasks with Radiological Images—0
A survey of underwater acoustic data classification methods using deep learning for shoreline surveillance—0
A Survey on Anomaly Detection for Technical Systems using LSTM Networks—0
A Survey on Computational Intelligence-based Transfer Learning—0
A Survey on Deep Domain Adaptation for LiDAR Perception—0
A Survey on Deep Industrial Transfer Learning in Fault Prognostics—0
A Survey on Deep Tabular Learning—0
A Survey on Deep Transfer Learning—0
A survey on domain adaptation theory: learning bounds and theoretical guarantees—0
A Survey on Incorporating Domain Knowledge into Deep Learning for Medical Image Analysis—0
A Survey on Heterogeneous Federated Learning—0
A Survey on Machine Learning Techniques for Auto Labeling of Video, Audio, and Text Data—0
A Survey on Model-based, Heuristic, and Machine Learning Optimization Approaches in RIS-aided Wireless Networks—0
A Survey on Multilingual Large Language Models: Corpora, Alignment, and Bias—0
A Survey on Self-supervised Pre-training for Sequential Transfer Learning in Neural Networks—0
A Survey on Transfer Learning in Natural Language Processing—0
A Survey on Visual Transfer Learning using Knowledge Graphs—0
Asymmetric Decision-Making in Online Knowledge Distillation:Unifying Consensus and Divergence—0
Asymmetric Transfer Hashing with Adaptive Bipartite Graph Learning—0
Asymptotic Midpoint Mixup for Margin Balancing and Moderate Broadening—0
Asynchronous Advantage Actor-Critic Agent for Starcraft II—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