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 8201–8250 of 10307 papers

TitleStatusHype
Neural Subgraph Isomorphism CountingCode0
Federated Imitation Learning: A Novel Framework for Cloud Robotic Systems with Heterogeneous Sensor Data—0
Cronus: Robust and Heterogeneous Collaborative Learning with Black-Box Knowledge Transfer—0
Intelligent Condition Based Monitoring Techniques for Bearing Fault Diagnosis—0
Data-Free Adversarial DistillationCode0
Progressive transfer learning for low frequency data prediction in full waveform inversion—0
Destruction of Image Steganography using Generative Adversarial NetworksCode0
Transfer Learning with Edge Attention for Prostate MRI Segmentation—0
Image Analytics for Legal Document Review: A Transfer Learning Approach—0
TransMatch: A Transfer-Learning Scheme for Semi-Supervised Few-Shot Learning—0
Predicting the real-valued distances between residue pairs for proteins—0
Research Frontiers in Transfer Learning -- a systematic and bibliometric review—0
Pruning by Explaining: A Novel Criterion for Deep Neural Network PruningCode0
Dreaming to Distill: Data-free Knowledge Transfer via DeepInversionCode0
Integration of Convolutional Neural Networks for Pulmonary Nodule Malignancy Assessment in a Lung Cancer Classification Pipeline—0
Lightweight and Robust Representation of Economic Scales from Satellite ImageryCode0
KonVid-150k: A Dataset for No-Reference Video Quality Assessment of Videos in-the-Wild—0
Transfer learning in hybrid classical-quantum neural networksCode0
A Transferable Adaptive Domain Adversarial Neural Network for Virtual Reality Augmented EMG-Based Gesture RecognitionCode0
A Broader Study of Cross-Domain Few-Shot LearningCode1
Iterative Dual Domain Adaptation for Neural Machine Translation—0
Automated Thalamic Nuclei Segmentation Using Multi-Planar Cascaded Convolutional Neural NetworksCode0
A Comparison of Architectures and Pretraining Methods for Contextualized Multilingual Word Embeddings—0
Multilingual is not enough: BERT for FinnishCode0
Targeted transfer learning to improve performance in small medical physics datasets—0
Common Voice: A Massively-Multilingual Speech CorpusCode1
Meta-Learning Initializations for Image SegmentationCode0
CLOSURE: Assessing Systematic Generalization of CLEVR ModelsCode0
Local Context Normalization: Revisiting Local NormalizationCode0
MineGAN: effective knowledge transfer from GANs to target domains with few imagesCode1
Self-Driving Car Steering Angle Prediction Based on Image RecognitionCode0
Transfer Learning-Based Outdoor Position Recovery with Telco Data—0
Unsupervised Transfer Learning via BERT Neuron Selection—0
Winning the Lottery with Continuous SparsificationCode0
Machine UnlearningCode0
Decision Support System for Detection and Classification of Skin Cancer using CNN—0
Security of Deep Learning Methodologies: Challenges and Opportunities—0
Personalized Patent Claim Generation and Measurement—0
Kernel learning for visual perceptionCode0
ClusterFit: Improving Generalization of Visual RepresentationsCode0
300 GHz Radar Object Recognition based on Deep Neural Networks and Transfer Learning—0
How Does an Approximate Model Help in Reinforcement Learning?—0
DeepEthnic: Multi-Label Ethnic Classification from Face Images—0
Self-Supervised Learning of Video-Induced Visual Invariances—0
Transfer Learning from an Auxiliary Discriminative Task for Unsupervised Anomaly Detection—0
AMUSED: A Multi-Stream Vector Representation Method for Use in Natural Dialogue—0
An Automated Deep Learning Approach for Bacterial Image Classification—0
Cross-lingual Pre-training Based Transfer for Zero-shot Neural Machine Translation—0
Different Set Domain Adaptation for Brain-Computer Interfaces: A Label Alignment ApproachCode1
Degenerative Adversarial NeuroImage Nets for Brain Scan Simulations: Application in Ageing and Dementia—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