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 8451–8475 of 10307 papers

TitleStatusHype
Animal Detection in Man-made EnvironmentsCode0
Deep Learning of Subsurface Flow via Theory-guided Neural Network—0
Class-imbalanced Domain Adaptation: An Empirical Odyssey—0
Semantic Segmentation of Skin Lesions using a Small Data Set—0
Healthcare NER Models Using Language Model Pretraining—0
Machine Learning for Scent: Learning Generalizable Perceptual Representations of Small Molecules—0
Semi-Supervised Histology Classification using Deep Multiple Instance Learning and Contrastive Predictive Coding—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
Analyzing the Forgetting Problem in the Pretrain-Finetuning of Dialogue Response Models—0
Conservation AI: Live Stream Analysis for the Detection of Endangered Species Using Convolutional Neural Networks and Drone Technology—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
Adaptive Transfer Learning of Multi-View Time Series Classification—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