Beast code in Giters

ikki kishida's repositories

neural_architecture_search_with_reinforcement_learning_appendix_a

Implementation of Appendix A (Neural Architecture Search with Reinforcement Learning: https://arxiv.org/abs/1611.01578) by chainer

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trainer

trainer for chainer

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squeeze_net

Implementation of SqueezeNet by chainer (SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size: https://arxiv.org/abs/1602.07360)

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adversarial-robustness-toolbox

This is a library dedicated to adversarial machine learning. Its purpose is to allow rapid crafting and analysis of attacks and defense methods for machine learning models. The Adversarial Robustness Toolbox provides an implementation for many state-of-the-art methods for attacking and defending classifiers. https://developer.ibm.com/code/open/projects/adversarial-robustness-toolbox/

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Pixel-to-Binary-Embedding

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active-bayesian-coresets

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active_osl

Code for "Active One-shot Learning"

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attention-is-all-you-need-pytorch

A PyTorch implementation of the Transformer model in "Attention is All You Need".

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BatchBALD

Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning.

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densenet-pytorch

A PyTorch Implementation for Densely Connected Convolutional Networks (DenseNets)

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dotfiles

dotfiles setting

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erroir

010

example_embedding

Deep Clustering for Unsupervised Learning of Visual Features

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iccv2019-inc

ICCV 2019 Overcoming Catastrophic Forgetting with Unlabeled Data in the Wild

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intro_vae

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is_receptive_field_necessary_to_big

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mmdetection

Fork of the MMDetection Toolbox containing the Robustness Benchmark from the paper "Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming" (merged)

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