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 7976–8000 of 10307 papers

TitleStatusHype
Convolutional Neural Networks for Classifying Melanoma Images—0
NIT-Agartala-NLP-Team at SemEval-2020 Task 8: Building Multimodal Classifiers to tackle Internet Humor—0
On Using Transfer Learning For Plant Disease Detection—0
An Evaluation of Recent Neural Sequence Tagging Models in Turkish Named Entity Recognition—0
Temperate Fish Detection and Classification: a Deep Learning based Approach—0
Ensemble Transfer Learning for the Prediction of Anti-Cancer Drug Response—0
A network-based transfer learning approach to improve sales forecasting of new products—0
Multi-Channel Transfer Learning of Chest X-ray Images for Screening of COVID-19—0
A Framework for Hierarchical Multilingual Machine Translation—0
High-Fidelity Accelerated MRI Reconstruction by Scan-Specific Fine-Tuning of Physics-Based Neural Networks—0
On the Generation of Medical Dialogues for COVID-19Code0
An Inductive Transfer Learning Approach using Cycle-consistent Adversarial Domain Adaptation with Application to Brain Tumor Segmentation—0
Segmenting Scientific Abstracts into Discourse Categories: A Deep Learning-Based Approach for Sparse Labeled DataCode0
SciANN: A Keras/Tensorflow wrapper for scientific computations and physics-informed deep learning using artificial neural networks—0
A Comparison of Few-Shot Learning Methods for Underwater Optical and Sonar Image Classification—0
Generative Model-driven Structure Aligning Discriminative Embeddings for Transductive Zero-shot Learning—0
Domain-specific loss design for unsupervised physical training: A new approach to modeling medical ML solutions—0
Visually Impaired Aid using Convolutional Neural Networks, Transfer Learning, and Particle Competition and CooperationCode0
Transfer Learning and Online Learning for Traffic Forecasting under Different Data Availability Conditions: Alternatives and Pitfalls—0
Automatic Organization of Neural Modules for Enhanced Collaboration in Neural Networks—0
Relatedness Measures to Aid the Transfer of Building Blocks among Multiple Tasks—0
Planning from Images with Deep Latent Gaussian Process DynamicsCode0
A Multifactorial Optimization Paradigm for Linkage Tree Genetic AlgorithmCode0
Subdomain Adaptation with Manifolds Discrepancy Alignment—0
End-to-end Whispered Speech Recognition with Frequency-weighted Approaches and Pseudo Whisper Pre-training—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