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 3051–3100 of 10307 papers

TitleStatusHype
Automaton Distillation: Neuro-Symbolic Transfer Learning for Deep Reinforcement Learning—0
Deep-Learning-Based Markerless Pose Estimation Systems in Gait Analysis: DeepLabCut Custom Training and the Refinement Function—0
An Analysis of Semantically-Aligned Speech-Text Embeddings—0
A New Mask R-CNN Based Method for Improved Landslide Detection—0
A New Method for Vehicle Logo Recognition Based on Swin Transformer—0
Distilling Generative-Discriminative Representations for Very Low-Resolution Face Recognition—0
Efficient Continual Adaptation of Pretrained Robotic Policy with Online Meta-Learned Adapters—0
Blockchain as an Enabler for Transfer Learning in Smart Environments—0
Block Toeplitz Sparse Precision Matrix Estimation for Large-Scale Interval-Valued Time Series Forecasting—0
Distilling Knowledge From a Deep Pose Regressor Network—0
BMIKE-53: Investigating Cross-Lingual Knowledge Editing with In-Context Learning—0
Automating the Surveillance of Mosquito Vectors from Trapped Specimens Using Computer Vision Techniques—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
A New Perspective on Smiling and Laughter Detection: Intensity Levels Matter—0
A Deep Value-network Based Approach for Multi-Driver Order Dispatching—0
Bone Marrow Cytomorphology Cell Detection using InceptionResNetV2—0
Distral: Robust Multitask Reinforcement Learning—0
Deep Learning-Based Image Kernel for Inductive Transfer—0
Automatic Wayang Ontology Construction using Relation Extraction from Free Text—0
Deep Learning Based HPV Status Prediction for Oropharyngeal Cancer Patients—0
Distributed Transfer Learning with 4th Gen Intel Xeon Processors—0
Distributionally Robust Transfer Learning—0
Deep Learning-based Extreme Heatwave Forecast—0
Distribution-Based Categorization of Classifier Transfer Learning—0
Boosting Automatic COVID-19 Detection Performance with Self-Supervised Learning and Batch Knowledge Ensembling—0
Distribution-Preserving k-Anonymity—0
Divergent representations of ethological visual inputs emerge from supervised, unsupervised, and reinforcement learning—0
Transfer or Self-Supervised? Bridging the Performance Gap in Medical Imaging—0
A Diagnostic Model for Acute Lymphoblastic Leukemia Using Metaheuristics and Deep Learning Methods—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
Boosting Deep Transfer Learning for COVID-19 Classification—0
DKT: Diverse Knowledge Transfer Transformer for Class Incremental Learning—0
Boosting Few-Shot Segmentation via Instance-Aware Data Augmentation and Local Consensus Guided Cross Attention—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
Sharpness-Aware Cross-Domain Recommendation to Cold-Start Users—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
Boosting Kidney Stone Identification in Endoscopic Images Using Two-Step Transfer Learning—0
DOCK: Detecting Objects by transferring Common-sense Knowledge—0
Efficient Discrete Physics-informed Neural Networks for Addressing Evolutionary Partial Differential Equations—0
Boosting multi-demographic federated learning for chest x-ray analysis using general-purpose self-supervised representations—0
Efficient Gravitational Wave Parameter Estimation via Knowledge Distillation: A ResNet1D-IAF Approach—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