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 8751–8800 of 10307 papers

TitleStatusHype
Continual Learning of Generative Models with Limited Data: From Wasserstein-1 Barycenter to Adaptive Coalescence—0
Continual Learning of Natural Language Processing Tasks: A Survey—0
Continual Learning on the Edge with TensorFlow Lite—0
Continual Learning with Adaptive Weights (CLAW)—0
Continual Learning with Dirichlet Generative-based Rehearsal—0
Continual Lifelong Learning with Neural Networks: A Review—0
Continually Detection, Rapidly React: Unseen Rumors Detection Based on Continual Prompt-Tuning—0
Towards continually learning new languages—0
Continual Meta-Reinforcement Learning for UAV-Aided Vehicular Wireless Networks—0
Continual Prompt Tuning for Dialog State Tracking—0
Continual Transfer Learning for Cross-Domain Click-Through Rate Prediction at Taobao—0
Continuous Domain Adaptation with Variational Domain-Agnostic Feature Replay—0
Continuous Emotion Recognition via Deep Convolutional Autoencoder and Support Vector Regressor—0
Continuous Emotion Recognition with Spatiotemporal Convolutional Neural Networks—0
Continuous Test-time Domain Adaptation for Efficient Fault Detection under Evolving Operating Conditions—0
Continuous Transfer Learning—0
Continuous Transfer Learning for UAV Communication-aware Trajectory Design—0
Continuous Transfer Learning with Label-informed Distribution Alignment—0
Continuous Word Embedding Fusion via Spectral Decomposition—0
Contradiction Detection in Persian Text—0
Learning Visual Models using a Knowledge Graph as a Trainer—0
Contrastive and Transfer Learning for Effective Audio Fingerprinting through a Real-World Evaluation Protocol—0
Contrastive Consolidation of Top-Down Modulations Achieves Sparsely Supervised Continual Learning—0
Contrastive Distillation Is a Sample-Efficient Self-Supervised Loss Policy for Transfer Learning—0
Contrastive Learning and Cycle Consistency-based Transductive Transfer Learning for Target Annotation—0
Contrastive Learning and Self-Training for Unsupervised Domain Adaptation in Semantic Segmentation—0
Contrastive learning for unsupervised medical image clustering and reconstruction—0
Contrastive Learning Meets Transfer Learning: A Case Study In Medical Image Analysis—0
Contrastive Representation Distillation via Multi-Scale Feature Decoupling—0
Contrastive Representation Learning Helps Cross-institutional Knowledge Transfer: A Study in Pediatric Ventilation Management—0
Breaking Writer's Block: Low-cost Fine-tuning of Natural Language Generation Models—0
Controlling Neural Collapse Enhances Out-of-Distribution Detection and Transfer Learning—0
Controlling the Precision-Recall Tradeoff in Differential Dependency Network Analysis—0
Control Theoretic Approach to Fine-Tuning and Transfer Learning—0
Control-Theoretic Techniques for Online Adaptation of Deep Neural Networks in Dynamical Systems—0
ConVAEr: Convolutional Variational AutoEncodeRs for incremental similarity learning—0
Convergence and Implicit Bias of Gradient Descent on Continual Linear Classification—0
Conversational Bots for Psychotherapy: A Study of Generative Transformer Models Using Domain-specific Dialogues—0
Conversion and Implementation of State-of-the-Art Deep Learning Algorithms for the Classification of Diabetic Retinopathy—0
ConvNets vs. Transformers: Whose Visual Representations are More Transferable?—0
Ladder Bottom-up Convolutional Bidirectional Variational Autoencoder for Image Translation of Dotted Arabic Expiration Dates—0
Convolutional Drift Networks for Video Classification—0
Convolutional Gated MLP: Combining Convolutions & gMLP—0
Convolutional-network models to predict wall-bounded turbulence from wall quantities—0
Convolutional Neural Network and Transfer Learning for High Impedance Fault Detection—0
Convolutional neural network classification of cancer cytopathology images: taking breast cancer as an example—0
Convolutional Neural Network for Stereotypical Motor Movement Detection in Autism—0
Convolutional neural network for Lyman break galaxies classification and redshift regression in DESI (Dark Energy Spectroscopic Instrument)—0
Convolutional Neural Network for Universal Sentence Embeddings—0
Convolutional Neural Networks and a Transfer Learning Strategy to Classify Parkinson's Disease from Speech in Three Different Languages—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