πŸ“š A collection of all the Deep Learning Metrics that I came across which are not accuracy/loss.

Related tags

Deep Learningcomputer-visiondeep-learningmetricsdnnpytorchcomparisonpytorch-implementation
Overview

Weird Deep Learning Metrics

πŸ“š A collection of all the Deep Learning Metrics that I came across which are not accuracy/loss.

Metric Description Notes
Churn Shows the disagreement between two model's prediction. Higher the churn, more the disagreement. Bounds: [0,1]. Takes two prediction arrays as inputs and retuns churn percentage. From this paper
Owner
Rahul Vigneswaran
Rahul Vigneswaran
GitHub Repository
Platform-agnostic AI Framework πŸ”₯

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Official code of our work, AVATAR: A Parallel Corpus for Java-Python Program Translation.

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Matlab Python Heuristic Battery Opt - SMOP conversion and manual conversion

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Learnable Motion Coherence for Correspondence Pruning

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Using NumPy to solve the equations of fluid mechanics together with Finite Differences, explicit time stepping and Chorin's Projection methods

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Leveraging Social Influence based on Users Activity Centers for Point-of-Interest Recommendation

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Detector for Log4Shell exploitation attempts

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Motion planning algorithms commonly used on autonomous vehicles. (path planning + path tracking)

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Image Captioning using CNN and Transformers

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Pipeline code for Sequential-GAM(Genome Architecture Mapping).

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Official respository for "Modeling Defocus-Disparity in Dual-Pixel Sensors", ICCP 2020

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A Tensorflow based library for Time Series Modelling with Gaussian Processes

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Code for the Paper "Diffusion Models for Handwriting Generation"

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