Model Optimization

69 papers with code • 0 benchmarks • 0 datasets

To Optimize already existing models in Training/Inferencing tasks.

Most implemented papers

Dataset Cartography: Mapping and Diagnosing Datasets with Training Dynamics

allenai/cartography EMNLP 2020

Experiments across four datasets show that these model-dependent measures reveal three distinct regions in the data map, each with pronounced characteristics.

Real-Time Scene Text Detection with Differentiable Binarization and Adaptive Scale Fusion

MhLiao/DB 21 Feb 2022

By incorporating the proposed DB and ASF with the segmentation network, our proposed scene text detector consistently achieves state-of-the-art results, in terms of both detection accuracy and speed, on five standard benchmarks.

Dynamic Scale Training for Object Detection

yukang2017/Stitcher 26 Apr 2020

We propose a Dynamic Scale Training paradigm (abbreviated as DST) to mitigate scale variation challenge in object detection.

Personalized Federated Learning with Moreau Envelopes

CharlieDinh/pFedMe NeurIPS 2020

Federated learning (FL) is a decentralized and privacy-preserving machine learning technique in which a group of clients collaborate with a server to learn a global model without sharing clients' data.

Iterative Surrogate Model Optimization (ISMO): An active learning algorithm for PDE constrained optimization with deep neural networks

kjetil-lye/iterative_surrogate_optimization 13 Aug 2020

We present a novel active learning algorithm, termed as iterative surrogate model optimization (ISMO), for robust and efficient numerical approximation of PDE constrained optimization problems.

Personalized Federated Learning with First Order Model Optimization

NVlabs/FedFomo ICLR 2021

While federated learning traditionally aims to train a single global model across decentralized local datasets, one model may not always be ideal for all participating clients.

PIXOR: Real-time 3D Object Detection from Point Clouds

DerrickXuNu/OpenCOOD CVPR 2018

Existing approaches are, however, expensive in computation due to high dimensionality of point clouds.

AutoScale: Learning to Scale for Crowd Counting and Localization

dk-liang/AutoScale 20 Dec 2019

A major issue is that the density map on dense regions usually accumulates density values from a number of nearby Gaussian blobs, yielding different large density values on a small set of pixels.

Do the Machine Learning Models on a Crowd Sourced Platform Exhibit Bias? An Empirical Study on Model Fairness

sumonbis/ML-Fairness 21 May 2020

Then, we have applied 7 mitigation techniques on these models and analyzed the fairness, mitigation results, and impacts on performance.

Multi-objective Asynchronous Successive Halving

mbilalzafar/fair-classification 23 Jun 2021

Hyperparameter optimization (HPO) is increasingly used to automatically tune the predictive performance (e. g., accuracy) of machine learning models.