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 6276–6300 of 10307 papers

TitleStatusHype
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
Group-disentangled Representation Learning with Weakly-Supervised Regularization—0
Domain Adaptation and Active Learning for Fine-Grained Recognition in the Field of Biodiversity—0
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
Bristle: Decentralized Federated Learning in Byzantine, Non-i.i.d. Environments—0
Augmenting Knowledge Distillation With Peer-To-Peer Mutual Learning For Model Compression—0
Knowledge-Guided Multiview Deep Curriculum Learning for Elbow Fracture ClassificationCode0
Improved Multilingual Language Model Pretraining for Social Media Text via Translation Pair PredictionCode0
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
SPAP: Simultaneous Demand Prediction and Planning for Electric Vehicle Chargers in a New CityCode0
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
Deep Transfer Learning & Beyond: Transformer Language Models in Information Systems Research—0
DECAR: Deep Clustering for learning general-purpose Audio RepresentationsCode0
Classical-to-Quantum Transfer Learning for Spoken Command Recognition Based on Quantum Neural Networks—0
Schrödinger's Tree -- On Syntax and Neural Language Models—0
Dataset Knowledge Transfer for Class-Incremental Learning without MemoryCode0
Cooperative Semi-Supervised Transfer Learning of Machine Reading Comprehension—0
A Variational Bayesian Approach to Learning Latent Variables for Acoustic Knowledge TransferCode0
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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