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 8451–8500 of 10307 papers

TitleStatusHype
Animal Detection in Man-made EnvironmentsCode0
Knowledge Transfer between Datasets for Learning-based Tissue Microstructure Estimation—0
Semantic Segmentation of Skin Lesions using a Small Data Set—0
Healthcare NER Models Using Language Model Pretraining—0
Semi-Supervised Histology Classification using Deep Multiple Instance Learning and Contrastive Predictive Coding—0
Class-imbalanced Domain Adaptation: An Empirical Odyssey—0
Machine Learning for Scent: Learning Generalizable Perceptual Representations of Small Molecules—0
Kernel computations from large-scale random features obtained by Optical Processing UnitsCode0
Word-level Embeddings for Cross-Task Transfer Learning in Speech ProcessingCode0
Localization of Fake News Detection via Multitask Transfer LearningCode0
Multiphase flow prediction with deep neural networks—0
Transformer-CNN: Fast and Reliable tool for QSARCode0
Online Bagging for Anytime Transfer Learning—0
Evolutionary Dynamic Multi-objective Optimization Via Regression Transfer Learning—0
Mutual Information-driven Subject-invariant and Class-relevant Deep Representation Learning in BCICode0
Understanding Social Networks using Transfer Learning—0
Conservation AI: Live Stream Analysis for the Detection of Endangered Species Using Convolutional Neural Networks and Drone Technology—0
Analyzing the Forgetting Problem in the Pretrain-Finetuning of Dialogue Response Models—0
Evolution of transfer learning in natural language processing—0
RiWalk: Fast Structural Node Embedding via Role IdentificationCode0
Transfer Learning for Algorithm Recommendation—0
Two-sample Testing Using Deep LearningCode0
Training Compact Models for Low Resource Entity Tagging using Pre-trained Language Models—0
Self-supervised Label Augmentation via Input TransformationsCode0
Manifold Embedded Knowledge Transfer for Brain-Computer InterfacesCode0
Adaptive Transfer Learning of Multi-View Time Series Classification—0
Actor Critic with Differentially Private Critic—0
Parameter-Transferred Wasserstein Generative Adversarial Network (PT-WGAN) for Low-Dose PET Image DenoisingCode0
Autonomous Navigation via Deep Reinforcement Learning for Resource Constraint Edge Nodes using Transfer LearningCode0
Model Fusion via Optimal TransportCode0
How to Not Measure Disentanglement—0
Deep Transfer Learning for Source Code ModelingCode0
Aff-Wild Database and AffWildNetCode0
DDTCDR: Deep Dual Transfer Cross Domain Recommendation—0
Conversational Transfer Learning for Emotion Recognition—0
Multi-modal Deep Analysis for Multimedia—0
Green Deep Reinforcement Learning for Radio Resource Management: Architecture, Algorithm Compression and Challenge—0
Automatic segmentation of texts into units of meaning for reading assistance—0
On-chip Few-shot Learning with Surrogate Gradient Descent on a Neuromorphic Processor—0
Learning protein conformational space by enforcing physics with convolutions and latent interpolations—0
Cross-lingual Alignment vs Joint Training: A Comparative Study and A Simple Unified FrameworkCode0
Imagined Value Gradients: Model-Based Policy Optimization with Transferable Latent Dynamics Models—0
A Closer Look At Feature Space Data Augmentation For Few-Shot Intent Classification—0
Linking emotions to behaviors through deep transfer learningCode0
Semi Few-Shot Attribute Translation—0
ATL: Autonomous Knowledge Transfer from Many Streaming ProcessesCode0
One-To-Many Multilingual End-to-end Speech Translation—0
Commonsense Knowledge Base Completion with Structural and Semantic ContextCode0
Graph Few-shot Learning via Knowledge TransferCode0
Semantic Preserving Generative Adversarial Models—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