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 9401–9450 of 10307 papers

TitleStatusHype
Distillation to Enhance the Portability of Risk Models Across Institutions with Large Patient Claims Database—0
Distilled ChatGPT Topic & Sentiment Modeling with Applications in Finance—0
Distilling Generative-Discriminative Representations for Very Low-Resolution Face Recognition—0
Distilling Knowledge From a Deep Pose Regressor Network—0
Distilling Localization for Self-Supervised Representation Learning—0
Distilling Named Entity Recognition Models for Endangered Species from Large Language Models—0
Distilling Normalizing Flows—0
Distilling Structured Knowledge for Text-Based Relational Reasoning—0
Distral: Robust Multitask Reinforcement Learning—0
Distributed Convolutional Neural Network Training on Mobile and Edge Clusters—0
Distributed Deep Transfer Learning by Basic Probability Assignment—0
Distributed Learning in Heterogeneous Environment: federated learning with adaptive aggregation and computation reduction—0
Distributed Transfer Learning with 4th Gen Intel Xeon Processors—0
Distributionally Robust Transfer Learning—0
Distribution-Based Categorization of Classifier Transfer Learning—0
Distribution-Preserving k-Anonymity—0
Divergent representations of ethological visual inputs emerge from supervised, unsupervised, and reinforcement learning—0
Diversified Mutual Learning for Deep Metric Learning—0
Divide, Conquer, and Combine: Mixture of Semantic-Independent Experts for Zero-Shot Dialogue State Tracking—0
DKT: Diverse Knowledge Transfer Transformer for Class Incremental Learning—0
DMCB at SemEval-2018 Task 1: Transfer Learning of Sentiment Classification Using Group LSTM for Emotion Intensity prediction—0
Δ-Patching: A Framework for Rapid Adaptation of Pre-trained Convolutional Networks without Base Performance Loss—0
Supporting Safety Analysis of Image-processing DNNs through Clustering-based Approaches—0
DNN Transfer Learning from Diversified Micro-Doppler for Motion Classification—0
Do Better ImageNet Models Transfer Better?—0
Do Better ImageNet Models Transfer Better... for Image Recommendation?—0
Doc2Im: document to image conversion through self-attentive embedding—0
DOCK: Detecting Objects by transferring Common-sense Knowledge—0
DocumentNet: Bridging the Data Gap in Document Pre-Training—0
Document-level Event Factuality Identification via Machine Reading Comprehension Frameworks with Transfer Learning—0
Do Deep Neural Networks Forget Facial Action Units? -- Exploring the Effects of Transfer Learning in Health Related Facial Expression Recognition—0
Does Adversarial Transferability Indicate Knowledge Transferability?—0
Does an LSTM forget more than a CNN? An empirical study of catastrophic forgetting in NLP—0
How Does an Approximate Model Help in Reinforcement Learning?—0
Does Non-COVID19 Lung Lesion Help? Investigating Transferability in COVID-19 CT Image Segmentation—0
Does Robustness on ImageNet Transfer to Downstream Tasks?—0
Does the Magic of BERT Apply to Medical Code Assignment? A Quantitative Study—0
Does Yoga Make You Happy? Analyzing Twitter User Happiness using Textual and Temporal Information—0
Dog Identification using Soft Biometrics and Neural Networks—0
Doing More with Less: Overcoming Data Scarcity for POI Recommendation via Cross-Region Transfer—0
Domain Adaptation and Active Learning for Fine-Grained Recognition in the Field of Biodiversity—0
Domain Adaptation and Transfer Learning in StochasticNets—0
Domain Adaptation-based Edge Computing for Cross-Conditions Fault Diagnosis—0
Domain Adaptation Broad Learning System Based on Locally Linear Embedding—0
Domain Adaptation by Topology Regularization—0
Domain Adaptation for Arabic Machine Translation: The Case of Financial Texts—0
Domain adaptation for holistic skin detection—0
Domain Adaptation for Inertial Measurement Unit-based Human Activity Recognition: A Survey—0
Domain Adaptation for Infection Prediction from Symptoms Based on Data from Different Study Designs and Contexts—0
Domain Adaptation for Reinforcement Learning on the Atari—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