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 7401–7450 of 10307 papers

TitleStatusHype
Accelerating Multi-Model Inference by Merging DNNs of Different Weights—0
Acceleration of Grokking in Learning Arithmetic Operations via Kolmogorov-Arnold Representation—0
Accumulating Knowledge for Lifelong Online Learning—0
Accurate Prostate Cancer Detection and Segmentation on Biparametric MRI using Non-local Mask R-CNN with Histopathological Ground Truth—0
A3E: Aligned and Augmented Adversarial Ensemble for Accurate, Robust and Privacy-Preserving EEG Decoding—0
A Centralized-Distributed Transfer Model for Cross-Domain Recommendation Based on Multi-Source Heterogeneous Transfer Learning—0
ACES -- Automatic Configuration of Energy Harvesting Sensors with Reinforcement Learning—0
A Checkpoint on Multilingual Misogyny Identification—0
Achieving Forgetting Prevention and Knowledge Transfer in Continual Learning—0
Achieving Pareto Optimality using Efficient Parameter Reduction for DNNs in Resource-Constrained Edge Environment—0
A Class of Geometric Structures in Transfer Learning: Minimax Bounds and Optimality—0
A Closer Look At Feature Space Data Augmentation For Few-Shot Intent Classification—0
A Closer Look at Knowledge Distillation with Features, Logits, and Gradients—0
A Closer Look at Model Adaptation using Feature Distortion and Simplicity Bias—0
A closer look at network resolution for efficient network design—0
ACNLP at SemEval-2020 Task 6: A Supervised Approach for Definition Extraction—0
A CNN-based approach to classify cricket bowlers based on their bowling actions—0
A Cognition-Affect Integrated Model of Emotion—0
A Collaborative Transfer Learning Framework for Cross-domain Recommendation—0
A Comparative Analysis of Transfer Learning-based Techniques for the Classification of Melanocytic Nevi—0
A Comparative Analysis Towards Melanoma Classification Using Transfer Learning by Analyzing Dermoscopic Images—0
A Comparative Evaluation Of Transformer Models For De-Identification Of Clinical Text Data—0
Supervised domain adaptation for building extraction from off-nadir aerial images—0
A Comparative Study of Open Source Computer Vision Models for Application on Small Data: The Case of CFRP Tape Laying—0
A Comparative Study of Transfer Learning for Emotion Recognition using CNN and Modified VGG16 Models—0
A Comparative Study of Western and Chinese Classical Music based on Soundscape Models—0
A comparative study of zero-shot inference with large language models and supervised modeling in breast cancer pathology classification—0
A Comparative Study on Transfer Learning and Distance Metrics in Semantic Clustering over the COVID-19 Tweets—0
A Compare-Aggregate Model with Latent Clustering for Answer Selection—0
A Comparison of Architectures and Pretraining Methods for Contextualized Multilingual Word Embeddings—0
A Comparison of Few-Shot Learning Methods for Underwater Optical and Sonar Image Classification—0
A Comparison of LSTM and BERT for Small Corpus—0
A Comparison of Methods for Neural Network Aggregation—0
A Comparison of Self-Supervised Pretraining Approaches for Predicting Disease Risk from Chest Radiograph Images—0
A Comparison of Transformer and Recurrent Neural Networks on Multilingual Neural Machine Translation—0
A Complete Recipe for Bayesian Knowledge Transfer: Object Tracking—0
A Composite Fault Diagnosis Model for NPPs Based on Bayesian-EfficientNet Module—0
A Comprehensive Analysis of Information Leakage in Deep Transfer Learning—0
A Comprehensive Evaluation Study on Risk Level Classification of Melanoma by Computer Vision on ISIC 2016-2020 Datasets—0
A Comprehensive Overview and Comparative Analysis on Deep Learning Models: CNN, RNN, LSTM, GRU—0
A Comprehensive Study of ImageNet Pre-Training for Historical Document Image Analysis—0
A comprehensive study on Blood Cancer detection and classification using Convolutional Neural Network—0
A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community—0
A Comprehensive Survey of Deep Transfer Learning for Anomaly Detection in Industrial Time Series: Methods, Applications, and Directions—0
A Comprehensive Survey of Federated Transfer Learning: Challenges, Methods and Applications—0
A Comprehensive Survey of Few-shot Learning: Evolution, Applications, Challenges, and Opportunities—0
A Comprehensive Survey of Multilingual Neural Machine Translation—0
A Comprehensive Survey on Architectural Advances in Deep CNNs: Challenges, Applications, and Emerging Research Directions—0
A Survey on Curriculum Learning—0
A Comprehensive Survey on Source-free Domain Adaptation—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