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 9051–9100 of 10307 papers

TitleStatusHype
Data-efficient Modeling of Optical Matrix Multipliers Using Transfer Learning—0
Data-Efficient Strategies for Probabilistic Voltage Envelopes under Network Contingencies—0
Data-Free Adversarial Knowledge Distillation for Graph Neural Networks—0
Data-Free Black-Box Federated Learning via Zeroth-Order Gradient Estimation—0
Data-Free Federated Class Incremental Learning with Diffusion-Based Generative Memory—0
Data-Free Knowledge Transfer: A Survey—0
Data Fusion of Deep Learned Molecular Embeddings for Property Prediction—0
Data Generation Using Pass-phrase-dependent Deep Auto-encoders for Text-Dependent Speaker Verification—0
Data Instance Prior for Transfer Learning in GANs—0
Data InStance Prior (DISP) in Generative Adversarial Networks—0
Data Optimisation for a Deep Learning Recommender System—0
Data Quality Monitoring through Transfer Learning on Anomaly Detection for the Hadron Calorimeters—0
Data Scarcity in Recommendation Systems: A Survey—0
DaTaSeg: Taming a Universal Multi-Dataset Multi-Task Segmentation Model—0
Data Selection for Efficient Model Update in Federated Learning—0
Data-selective Transfer Learning for Multi-Domain Speech Recognition—0
Dataset and Performance Comparison of Deep Learning Architectures for Plum Detection and Robotic Harvesting—0
DATSING: Data Augmented Time Series Forecasting with Adversarial Domain Adaptation—0
DCNNs: A Transfer Learning comparison of Full Weapon Family threat detection for Dual-Energy X-Ray Baggage Imagery—0
DDTCDR: Deep Dual Transfer Cross Domain Recommendation—0
(DE)^2 CO: Deep Depth Colorization—0
Dealing with Adversarial Player Strategies in the Neural Network Game iNNk through Ensemble Learning—0
DEArt: Dataset of European Art—0
Dec-Adapter: Exploring Efficient Decoder-Side Adapter for Bridging Screen Content and Natural Image Compression—0
Decentralized Federated Learning via Mutual Knowledge Transfer—0
Decentralized Reinforcement Learning: Global Decision-Making via Local Economic Transactions—0
Deception Detection with Feature-Augmentation by soft Domain Transfer—0
Deciphering and Optimizing Multi-Task Learning: a Random Matrix Approach—0
Decision Support System for Detection and Classification of Skin Cancer using CNN—0
Decoding Working-Memory Load During n-Back Task Performance from High Channel NIRS Data—0
Decomposable Probability-of-Success Metrics in Algorithmic Search—0
Decomposed Cross-modal Distillation for RGB-based Temporal Action Detection—0
Decomposition-Based Transfer Distance Metric Learning for Image Classification—0
Decoupled Box Proposal and Featurization with Ultrafine-Grained Semantic Labels Improve Image Captioning and Visual Question Answering—0
Decoupled classifiers for fair and efficient machine learning—0
Decouple Non-parametric Knowledge Distillation For End-to-end Speech Translation—0
DEEGITS: Deep Learning based Framework for Measuring Heterogenous Traffic State in Challenging Traffic Scenarios—0
Deep 3D Face Identification—0
Deep 3D-Zoom Net: Unsupervised Learning of Photo-Realistic 3D-Zoom—0
Deep Adaptation of Adult-Child Facial Expressions by Fusing Landmark Features—0
Deep Bag-of-Sub-Emotions for Depression Detection in Social Media—0
DeepBeat: A multi-task deep learning approach to assess signal quality and arrhythmia detection in wearable devices—0
Deep Clustering of Remote Sensing Scenes through Heterogeneous Transfer Learning—0
Deep CNNs for large scale species classification—0
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning—0
Deep Convolutional Neural Networks for Interpretable Analysis of EEG Sleep Stage Scoring—0
Deep cross-domain building extraction for selective depth estimation from oblique aerial imagery—0
Deep Cross Networks with Aesthetic Preference for Cross-domain Recommendation—0
Deep Decomposition for Stochastic Normal-Abnormal Transport—0
Deep Discriminative Fine-Tuning for Cancer Type Classification—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