Model Optimization
88 papers with code • 0 benchmarks • 0 datasets
To Optimize already existing models in Training/Inferencing tasks.
Benchmarks
These leaderboards are used to track progress in Model Optimization
Most implemented papers
ML4Chem: A Machine Learning Package for Chemistry and Materials Science
muammar/ml4chem
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It provides an extendable platform to develop and deploy machine learning models and pipelines and is targeted to the non-expert and expert users.
A Fast Fully Octave Convolutional Neural Network for Document Image Segmentation
ricardobnjunior/OctHU-PageScan
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In this context, we investigated a method based on U-Net to detect the document edges and text regions in ID images.
Rank Position Forecasting in Car Racing
Forecasting is challenging since uncertainty resulted from exogenous factors exists.
Spatio-temporal Multi-task Learning for Cardiac MRI Left Ventricle Quantification
sulaimanvesal/CardiacQuanNet
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In this paper, we propose a spatio-temporal multi-task learning approach to obtain a complete set of measurements quantifying cardiac LV morphology, regional-wall thickness (RWT), and additionally detecting the cardiac phase cycle (systole and diastole) for a given 3D Cine-magnetic resonance (MR) image sequence.
Multitask Learning for Emotion and Personality Detection
npuliyang/Personality-Detection-MTL
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In recent years, deep learning-based automated personality trait detection has received a lot of attention, especially now, due to the massive digital footprints of an individual.
Loss Function Discovery for Object Detection via Convergence-Simulation Driven Search
PerdonLiu/CSE-Autoloss
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ICLR 2021
For object detection, the well-established classification and regression loss functions have been carefully designed by considering diverse learning challenges.
Unleashing the Power of Contrastive Self-Supervised Visual Models via Contrast-Regularized Fine-Tuning
vanint/core-tuning
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NeurIPS 2021
In this paper, we investigate whether applying contrastive learning to fine-tuning would bring further benefits, and analytically find that optimizing the contrastive loss benefits both discriminative representation learning and model optimization during fine-tuning.
Few-Shot Graph Learning for Molecular Property Prediction
The recent success of graph neural networks has significantly boosted molecular property prediction, advancing activities such as drug discovery.
DEUP: Direct Epistemic Uncertainty Prediction
MJ10/DEUP
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Epistemic Uncertainty is a measure of the lack of knowledge of a learner which diminishes with more evidence.
CMUA-Watermark: A Cross-Model Universal Adversarial Watermark for Combating Deepfakes
Then, we design a two-level perturbation fusion strategy to alleviate the conflict between the adversarial watermarks generated by different facial images and models.
