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 5651–5700 of 10307 papers

TitleStatusHype
MoKD: Multi-Task Optimization for Knowledge Distillation—0
Molecular Subtype Prediction for Breast Cancer Using H&E Specialized Backbone—0
MOMA:Distill from Self-Supervised Teachers—0
MomentsNeRF: Leveraging Orthogonal Moments for Few-Shot Neural Rendering—0
Monaural speech enhancement on drone via Adapter based transfer learning—0
Monitoring crop phenology with street-level imagery using computer vision—0
Monkey Transfer Learning Can Improve Human Pose Estimation—0
Mono2Stereo: Monocular Knowledge Transfer for Enhanced Stereo Matching—0
Monocular 3D Human Pose Estimation In The Wild Using Improved CNN Supervision—0
Monolingual and Cross-Lingual Knowledge Transfer for Topic Classification—0
MorisienMT: A Dataset for Mauritian Creole Machine Translation—0
Morpheo: Traceable Machine Learning on Hidden data—0
Motion-Augmented Self-Training for Video Recognition at Smaller Scale—0
Motion Blur removal via Coupled Autoencoder—0
Motion-Guided Masking for Spatiotemporal Representation Learning—0
Mouth Articulation-Based Anchoring for Improved Cross-Corpus Speech Emotion Recognition—0
Moving from Cross-Project Defect Prediction to Heterogeneous Defect Prediction: A Partial Replication Study—0
MRIo3DS-Net: A Mutually Reinforcing Images to 3D Surface RNN-like framework for model-adaptation indoor 3D reconstruction—0
Intelligent Condition Based Monitoring Techniques for Bearing Fault Diagnosis—0
MSdocTr-Lite: A Lite Transformer for Full Page Multi-script Handwriting Recognition—0
MSIT\_SRIB at MEDIQA 2019: Knowledge Directed Multi-task Framework for Natural Language Inference in Clinical Domain.—0
MTG: A Benchmarking Suite for Multilingual Text Generation—0
MTI-Net: A Multi-Target Speech Intelligibility Prediction Model—0
MuCaLe-Net: Multi Categorical-Level Networks to Generate More Discriminating Features—0
MUCS@Text-LT-EDI@ACL 2022: Detecting Sign of Depression from Social Media Text using Supervised Learning Approach—0
Multi-Action Dialog Policy Learning with Interactive Human Teaching—0
MultiADE: A Multi-domain Benchmark for Adverse Drug Event Extraction—0
Multi-Agent Based Transfer Learning for Data-Driven Air Traffic Applications—0
Multi-Agent Collaboration for Multilingual Code Instruction Tuning—0
Multi-Agent Policy Transfer via Task Relationship Modeling—0
Improved Reinforcement Learning in Cooperative Multi-agent Environments Using Knowledge Transfer—0
Multi-agent Reinforcement Learning with Sparse Interactions by Negotiation and Knowledge Transfer—0
Multi-Agent Transfer Learning in Reinforcement Learning-Based Ride-Sharing Systems—0
Multi-Agent Transfer Learning via Temporal Contrastive Learning—0
MultiBalance: Multi-Objective Gradient Balancing in Industrial-Scale Multi-Task Recommendation System—0
Multi-batch Reinforcement Learning via Sample Transfer and Imitation Learning—0
Multi-Cell Compositional LSTM for NER Domain Adaptation—0
Multicenter automatic detection of invasive carcinoma on breast whole slide images—0
Domain generalization in deep learning for contrast-enhanced imaging—0
Multichannel Attention Networks with Ensembled Transfer Learning to Recognize Bangla Handwritten Charecter—0
Multi-Channel CNN-based Object Detection for Enhanced Situation Awareness—0
Multi-Channel Transfer Learning of Chest X-ray Images for Screening of COVID-19—0
Multi-Classification of Brain Tumor Images Using Transfer Learning Based Deep Neural Network—0
Multi-class Semantic Segmentation of Skin Lesions via Fully Convolutional Networks—0
Multi-class Skin Cancer Classification Architecture Based on Deep Convolutional Neural Network—0
Multi-Class Zero-Shot Learning for Artistic Material Recognition—0
MultiConfederated Learning: Inclusive Non-IID Data handling with Decentralized Federated Learning—0
Multi-Dataset Multi-Task Learning for COVID-19 Prognosis—0
Multi-dataset synergistic in supervised learning to pre-label structural components in point clouds from shell construction scenes—0
Multidomain Document Layout Understanding using Few Shot Object Detection—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