Explaining Deep Neural Networks - A comparison of different CAM methods based on an insect data set

Last update: Feb 06, 2022

Overview

Explaining Deep Neural Networks - A comparison of different CAM methods based on an insect data set

This is the repository for the Deep Learning project "Explaining Deep Neural Networks - A comparison of different CAM methods based on an insect data set" (2022).

For information about the data set and the trained weights contact our team or the owner of this repository (Robert Krug).

The stored weights of the different network configuration can be accessed here.

Owner

Robert Krug

GitHub Repository

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Explaining Deep Neural Networks - A comparison of different CAM methods based on an insect data set

Related tags

Overview

Explaining Deep Neural Networks - A comparison of different CAM methods based on an insect data set

Owner

Robert Krug

Bounding Wasserstein distance with couplings

Public Models considered for emotion estimation from EEG

A custom-designed Spider Robot trained to walk using Deep RL in a PyBullet Simulation

VarCLR: Variable Semantic Representation Pre-training via Contrastive Learning

Continuous Security Group Rule Change Detection & Response at scale

An investigation project for SISR.

Keras implementation of PersonLab for Multi-Person Pose Estimation and Instance Segmentation.

BlueFog Tutorials

[NeurIPS 2020] Blind Video Temporal Consistency via Deep Video Prior

Detectron2-FC a fast construction platform of neural network algorithm based on detectron2

Learning-based agent for Google Research Football

Efficient Two-Step Networks for Temporal Action Segmentation (Neurocomputing 2021)

Data reduction pipeline for KOALA on the AAT.

Very large and sparse networks appear often in the wild and present unique algorithmic opportunities and challenges for the practitioner

This code provides a PyTorch implementation for OTTER (Optimal Transport distillation for Efficient zero-shot Recognition), as described in the paper.

Official Tensorflow implementation of U-GAT-IT: Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation (ICLR 2020)

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