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

Overview

Weird Deep Learning Metrics

📚 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
Conservative Q Learning for Offline Reinforcement Reinforcement Learning in JAX

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Subgraph Based Learning of Contextual Embedding

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This code is an implementation for Singing TTS.

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Bag of Tricks for Natural Policy Gradient Reinforcement Learning

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Source code for our paper "Do Not Trust Prediction Scores for Membership Inference Attacks"

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The codes I made while I practiced various TensorFlow examples

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Corruption Invariant Learning for Re-identification

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This library is a location of the LegacyLogger for PyTorch Lightning.

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Collection of NLP model explanations and accompanying analysis tools

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rastrainer is a QGIS plugin to training remote sensing semantic segmentation model based on PaddlePaddle.

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Code release for General Greedy De-bias Learning

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code for our ECCV-2020 paper: Self-supervised Video Representation Learning by Pace Prediction

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This is a Python wrapper for TA-LIB based on Cython instead of SWIG.

TA-Lib This is a Python wrapper for TA-LIB based on Cython instead of SWIG. From the homepage: TA-Lib is widely used by trading software developers re

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social humanoid robots with GPGPU and IoT

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Real time sign language recognition

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Mohit Kaushik 6 Jun 13, 2022
Localized representation learning from Vision and Text (LoVT)

Localized Vision-Text Pre-Training Contrastive learning has proven effective for pre- training image models on unlabeled data and achieved great resul

Philip Müller 10 Dec 07, 2022
A PaddlePaddle version image model zoo.

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JumpDiff: Non-parametric estimator for Jump-diffusion processes for Python

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Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more

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code for EMNLP 2019 paper Text Summarization with Pretrained Encoders

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