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 7151–7200 of 10307 papers

TitleStatusHype
Transfer Learning and Augmentation for Word Sense Disambiguation—0
Extracting Pasture Phenotype and Biomass Percentages using Weakly Supervised Multi-target Deep Learning on a Small Dataset—0
Applying Transfer Learning for Improving Domain-Specific Search Experience Using Query to Question Similarity—0
Phase Transitions in Transfer Learning for High-Dimensional Perceptrons—0
Learn Dynamic-Aware State Embedding for Transfer Learning—0
A Robust Illumination-Invariant Camera System for Agricultural Applications—0
COVID-19: Comparative Analysis of Methods for Identifying Articles Related to Therapeutics and Vaccines without Using Labeled Data—0
An Automatic System to Monitor the Physical Distance and Face Mask Wearing of Construction Workers in COVID-19 Pandemic—0
End-to-End Video Question-Answer Generation with Generator-Pretester NetworkCode0
A Framework for Fast Scalable BNN Inference using Googlenet and Transfer Learning—0
Coreference Resolution in Research Papers from Multiple DomainsCode0
SpotPatch: Parameter-Efficient Transfer Learning for Mobile Object Detection—0
Towards Network Traffic Monitoring Using Deep Transfer Learning—0
Exploring Transfer Learning on Face Recognition of Dark Skinned, Low Quality and Low Resource Face Data—0
Comparative study on different Deep Learning models for Skin Lesion Classification using transfer learning approachCode0
Contextual Transformation Networks for Online Continual Learning—0
Cross-lingual Transfer Learning for Pre-trained Contextualized Language Models—0
CCT-Net: Category-Invariant Cross-Domain Transfer for Medical Single-to-Multiple Disease Diagnosis—0
Probabilistic Meta-Learning for Bayesian Optimization—0
Pre-training Text-to-Text Transformers to Write and Reason with Concepts—0
Deciphering and Optimizing Multi-Task Learning: a Random Matrix Approach—0
Adaptive Adversarial Network for Source-Free Domain Adaptation—0
Continuous Transfer Learning—0
Knowledge Mining and Transferring for Domain Adaptive Object DetectionCode0
Re-examining Routing Networks for Multi-task Learning—0
Adaptive Label Noise Cleaning With Meta-Supervision for Deep Face Recognition—0
A Deep Learning Approach for Diabetic Retinopathy detection using Transfer Learning—0
MoCo-Pretraining Improves Representations and Transferability of Chest X-ray Models—0
Fast and Efficient DNN Deployment via Deep Gaussian Transfer Learning—0
Self-Supervised Transfer Learning for Hand Mesh Recovery From Binocular Images—0
Generalization in data-driven models of primary visual cortex—0
Network-Agnostic Knowledge Transfer from Latent Dataset for Medical Image Segmentation—0
Explicit Connection Distillation—0
Exploring the Uncertainty Properties of Neural Networks’ Implicit Priors in the Infinite-Width Limit—0
Constraining Latent Space to Improve Deep Self-Supervised e-Commerce Products Embeddings for Downstream Tasks—0
Efficient Graph Neural Architecture Search—0
AT-GAN: An Adversarial Generative Model for Non-constrained Adversarial Examples—0
P-Swish: Activation Function with Learnable Parameters Based on Swish Activation Function in Deep Learning—0
Compute- and Memory-Efficient Reinforcement Learning with Latent Experience Replay—0
An Euler-based GAN for time series—0
Efficient Learning of Less Biased Models with Transfer Learning—0
CIZSL++: Creativity Inspired Generative Zero-Shot LearningCode0
CDS: Cross-Domain Self-Supervised Pre-Training—0
Counterfactual Thinking for Long-tailed Information Extraction—0
Can Students Outperform Teachers in Knowledge Distillation based Model Compression?—0
Transferable Unsupervised Robust Representation Learning—0
Unified Principles For Multi-Source Transfer Learning Under Label Shifts—0
Zero-shot Transfer Learning for Gray-box Hyper-parameter Optimization—0
XLA: A Robust Unsupervised Data Augmentation Framework for Cross-Lingual NLP—0
Unsupervised Task Clustering for Multi-Task Reinforcement LearningCode0
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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