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 3151–3175 of 10307 papers

TitleStatusHype
Iterative self-transfer learning: A general methodology for response time-history prediction based on small dataset—0
Heterogeneous Continual Learning—0
SMC-UDA: Structure-Modal Constraint for Unsupervised Cross-Domain Renal Segmentation—0
PersonaPKT: Building Personalized Dialogue Agents via Parameter-efficient Knowledge Transfer—0
Robustness and Generalization Performance of Deep Learning Models on Cyber-Physical Systems: A Comparative StudyCode0
One-for-All: Generalized LoRA for Parameter-Efficient Fine-tuningCode2
Monolingual and Cross-Lingual Knowledge Transfer for Topic Classification—0
EriBERTa: A Bilingual Pre-Trained Language Model for Clinical Natural Language Processing—0
Parameter-efficient Dysarthric Speech Recognition Using Adapter Fusion and Householder Transformation—0
Generating Synthetic Datasets by Interpolating along Generalized Geodesics—0
A Brief Review of Hypernetworks in Deep LearningCode0
Differentiable Multi-Fidelity Fusion: Efficient Learning of Physics Simulations with Neural Architecture Search and Transfer Learning—0
VBSF-TLD: Validation-Based Approach for Soft Computing-Inspired Transfer Learning in Drone Detection—0
An information-Theoretic Approach to Semi-supervised Transfer Learning—0
Enhancing Low Resource NER Using Assisting Language And Transfer Learning—0
PoET: A generative model of protein families as sequences-of-sequencesCode1
SARN: Structurally-Aware Recurrent Network for Spatio-Temporal DisaggregationCode0
End-to-End Neural Network Compression via _1_2 Regularized Latency Surrogates—0
Understanding the Benefits of Image Augmentations—0
Emotion Detection from EEG using Transfer Learning—0
Customizing General-Purpose Foundation Models for Medical Report Generation—0
Generalization Performance of Transfer Learning: Overparameterized and Underparameterized Regimes—0
T3L: Translate-and-Test Transfer Learning for Cross-Lingual Text ClassificationCode0
Prompter: Zero-shot Adaptive Prefixes for Dialogue State Tracking Domain AdaptationCode1
AutoML Systems For Medical Imaging—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