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 6751–6800 of 10307 papers

TitleStatusHype
Phase Transitions in Transfer Learning for High-Dimensional Perceptrons—0
End-to-End Video Question-Answer Generation with Generator-Pretester NetworkCode0
An Automatic System to Monitor the Physical Distance and Face Mask Wearing of Construction Workers in COVID-19 Pandemic—0
COVID-19: Comparative Analysis of Methods for Identifying Articles Related to Therapeutics and Vaccines without Using Labeled Data—0
SpotPatch: Parameter-Efficient Transfer Learning for Mobile Object Detection—0
Improving Portuguese Semantic Role Labeling with Transformers and Transfer LearningCode1
Towards Network Traffic Monitoring Using Deep Transfer Learning—0
Coreference Resolution in Research Papers from Multiple DomainsCode0
A Framework for Fast Scalable BNN Inference using Googlenet and Transfer Learning—0
Comparative study on different Deep Learning models for Skin Lesion Classification using transfer learning approachCode0
Exploring Transfer Learning on Face Recognition of Dark Skinned, Low Quality and Low Resource Face Data—0
Few-shot Image Classification: Just Use a Library of Pre-trained Feature Extractors and a Simple ClassifierCode1
Adaptive Adversarial Network for Source-Free Domain Adaptation—0
Adaptive Label Noise Cleaning With Meta-Supervision for Deep Face Recognition—0
Knowledge Mining and Transferring for Domain Adaptive Object DetectionCode0
CCT-Net: Category-Invariant Cross-Domain Transfer for Medical Single-to-Multiple Disease Diagnosis—0
Self-Supervised Transfer Learning for Hand Mesh Recovery From Binocular Images—0
Fast and Efficient DNN Deployment via Deep Gaussian Transfer Learning—0
CDS: Cross-Domain Self-Supervised Pre-Training—0
P-Swish: Activation Function with Learnable Parameters Based on Swish Activation Function in Deep Learning—0
A Deep Learning Approach for Diabetic Retinopathy detection using Transfer Learning—0
CIZSL++: Creativity Inspired Generative Zero-Shot LearningCode0
WARP: Word-level Adversarial ReProgrammingCode1
Efficient Learning of Less Biased Models with Transfer Learning—0
Generalization in data-driven models of primary visual cortex—0
Continuous Transfer Learning—0
IEPT: Instance-Level and Episode-Level Pretext Tasks for Few-Shot LearningCode1
Explicit Connection Distillation—0
Compute- and Memory-Efficient Reinforcement Learning with Latent Experience Replay—0
Constraining Latent Space to Improve Deep Self-Supervised e-Commerce Products Embeddings for Downstream Tasks—0
Deciphering and Optimizing Multi-Task Learning: a Random Matrix Approach—0
Can Students Outperform Teachers in Knowledge Distillation based Model Compression?—0
Cross-lingual Transfer Learning for Pre-trained Contextualized Language Models—0
TransNAS-Bench-101: Improving Transferrability and Generalizability of Cross-Task Neural Architecture SearchCode1
Unsupervised Task Clustering for Multi-Task Reinforcement LearningCode0
XLA: A Robust Unsupervised Data Augmentation Framework for Cross-Lingual NLP—0
Transferable Unsupervised Robust Representation Learning—0
Unified Principles For Multi-Source Transfer Learning Under Label Shifts—0
MoCo-Pretraining Improves Representations and Transferability of Chest X-ray Models—0
Re-examining Routing Networks for Multi-task Learning—0
Efficient Graph Neural Architecture Search—0
Exploring the Uncertainty Properties of Neural Networks’ Implicit Priors in the Infinite-Width Limit—0
Contextual Transformation Networks for Online Continual Learning—0
Pre-training Text-to-Text Transformers to Write and Reason with Concepts—0
Probabilistic Meta-Learning for Bayesian Optimization—0
Counterfactual Thinking for Long-tailed Information Extraction—0
AT-GAN: An Adversarial Generative Model for Non-constrained Adversarial Examples—0
Network-Agnostic Knowledge Transfer from Latent Dataset for Medical Image Segmentation—0
An Euler-based GAN for time series—0
Zero-shot Transfer Learning for Gray-box Hyper-parameter Optimization—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