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 6251–6300 of 10307 papers

TitleStatusHype
Successor Feature Representations—0
A deep convolutional neural network for classification of Aedes albopictus mosquitoes—0
AI-Powered Semantic Segmentation and Fluid Volume Calculation of Lung CT images in Covid-19 Patients—0
Domain Agnostic Few-Shot Learning For Document Intelligence—0
Deep Learning Analysis of Cardiac MRI in Legacy Datasets: Multi-Ethnic Study of Atherosclerosis—0
Generating Table Vector Representations—0
Revisit Multimodal Meta-Learning through the Lens of Multi-Task LearningCode0
Pay attention to emoji: Feature Fusion Network with EmoGraph2vec Model for Sentiment Analysis—0
NIDA-CLIFGAN: Natural Infrastructure Damage Assessment through Efficient Classification Combining Contrastive Learning, Information Fusion and Generative Adversarial Networks—0
OpeNPDN: A Neural-network-based Framework for Power Delivery Network Synthesis—0
Deep Transfer Learning for Multi-source Entity Linkage via Domain AdaptationCode0
Transfer learning with causal counterfactual reasoning in Decision Transformers—0
Zero-Shot Action Recognition from Diverse Object-Scene CompositionsCode0
Transferring Domain-Agnostic Knowledge in Video Question Answering—0
Deep Integrated Pipeline of Segmentation Guided Classification of Breast Cancer from Ultrasound Images—0
Improving the efficacy of Deep Learning models for Heart Beat detection on heterogeneous datasetsCode0
A Metalearning Approach for Physics-Informed Neural Networks (PINNs): Application to Parameterized PDEs—0
BioIE: Biomedical Information Extraction with Multi-head Attention Enhanced Graph Convolutional Network—0
Self-Supervised Knowledge Transfer via Loosely Supervised Auxiliary Tasks—0
Latent-Insensitive autoencoders for Anomaly Detection—0
Age and Gender Prediction using Deep CNNs and Transfer Learning—0
Covariance-Generalized Matching Component Analysis for Data Fusion and Transfer Learning—0
Review-Based Domain Disentanglement without Duplicate Users or Contexts for Cross-Domain Recommendation—0
Novel coronavirus pneumonia lesion segmentation in CT images—0
A Deep Reinforcement Learning Approach for Audio-based Navigation and Audio Source Localization in Multi-speaker Environments—0
Group-disentangled Representation Learning with Weakly-Supervised Regularization—0
Parametric Variational Linear Units (PVLUs) in Deep Convolutional Networks—0
Benchmarking of Lightweight Deep Learning Architectures for Skin Cancer Classification using ISIC 2017 Dataset—0
Domain Adaptation via Maximizing Surrogate Mutual InformationCode0
Logical Activation Functions: Logit-space equivalents of Probabilistic Boolean Operators—0
Multimodal-Boost: Multimodal Medical Image Super-Resolution using Multi-Attention Network with Wavelet Transform—0
Domain Adaptation and Active Learning for Fine-Grained Recognition in the Field of Biodiversity—0
Augmenting Knowledge Distillation With Peer-To-Peer Mutual Learning For Model Compression—0
Bristle: Decentralized Federated Learning in Byzantine, Non-i.i.d. Environments—0
Improved Multilingual Language Model Pretraining for Social Media Text via Translation Pair PredictionCode0
Knowledge-Guided Multiview Deep Curriculum Learning for Elbow Fracture ClassificationCode0
Learning Rich Nearest Neighbor Representations from Self-supervised Ensembles—0
Test time Adaptation through Perturbation RobustnessCode0
Zero-Shot Physics-Guided Deep Learning for Subject-Specific MRI Reconstruction—0
Deep Transfer Learning & Beyond: Transformer Language Models in Information Systems Research—0
Distinguishing Natural and Computer-Generated Images using Multi-Colorspace fused EfficientNetCode0
Sim-to-Real Transfer in Multi-agent Reinforcement Networking for Federated Edge Computing—0
Incremental Cross-Domain Adaptation for Robust Retinopathy Screening via Bayesian Deep LearningCode0
SPAP: Simultaneous Demand Prediction and Planning for Electric Vehicle Chargers in a New CityCode0
Schrödinger's Tree -- On Syntax and Neural Language Models—0
Classical-to-Quantum Transfer Learning for Spoken Command Recognition Based on Quantum Neural Networks—0
DECAR: Deep Clustering for learning general-purpose Audio RepresentationsCode0
Cooperative Semi-Supervised Transfer Learning of Machine Reading Comprehension—0
MTG: A Benchmarking Suite for Multilingual Text Generation—0
Cross-lingual Constituency Parsing with Linguistic Typology Knowledge—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