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 3076–3100 of 10307 papers

TitleStatusHype
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