MelGAN test on audio decoding

The original work URL: https://github.com/descriptinc/melgan-neurips Previous works have found that generating coherent raw audio waveforms with GANs is challenging. In this paper, we show that it is possible to train GANs reliably to generate high quality coherent waveforms by introducing a set of architectural changes and simple training techniques. Subjective evaluation metric (Mean Opinion Score, or MOS) shows the effectiveness of the proposed approach for high quality mel-spectrogram inversion. To establish the generality of the proposed techniques, we show qualitative […]

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Digdata presented ‘BrandX’ as a clothing brand that wants to know the best places to set up a ‘pop up’ store

Digdata presented ‘BrandX’ as a clothing brand that wants to know the best places to set up a ‘pop up’ store. I used the dataset given to write a program that ranks these places. Digdata – Analysis/Final Report I created a program that goes through the excel spreadsheet and analysis the data in a selection of categories. The code would rank each of the retail centres within each category and add points accordingly. The code then adjusted the total point […]

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Detector for Log4Shell exploitation attempts

Detector for Log4Shell exploitation attempts Idea The problem with the log4j CVE-2021-44228 exploitation is that the string can be heavily obfuscated in many different ways. It is impossible to cover all possible forms with a reasonable regular expression. The idea behind this detector is that the respective characters have to appear in a log line in a certain order to match. Split up into a list it would look like this: [‘$’, ‘{‘, ‘j’, ‘n’, ‘d’, ‘i’, ‘:’, ‘l’, ‘d’, […]

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TensorFlow CNN for fast style transfer

Fast Style Transfer in TensorFlow Add styles from famous paintings to any photo in a fraction of a second! It takes 100ms on a 2015 Titan X to style the MIT Stata Center (1024×680) like Udnie, by Francis Picabia. Our implementation is based off of a combination of Gatys’ A Neural Algorithm of Artistic Style, Johnson’s Perceptual Losses for Real-Time Style Transfer and Super-Resolution, and Ulyanov’s Instance Normalization.THe repository i based on https://github.com/lengstrom/fast-style-transfer.git. Image Stylization We added styles from various […]

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in-progress decompilation of Gauntlet Legends decompression code on the N64

A in-progress decompilation of Gauntlet-Legends (N64) decompression code. This project currently supports the US release. Building (Linux) The build process has the following package requirements: git build-essential binutils-mips-linux-gnu python3 Under a Debian based distribution, you can install these with the following commands: sudo apt update sudo apt install git build-essential binutils-mips-linux-gnu python3 Clone the repository Clone https://github.com/Drahsid/gauntlet-legends.git in whatever directory you wish. Make sure to initialize the submodules! git clone https://github.com/Drahsid/gauntlet-legends.git –recursive cd gauntlet-legends git submodule init Install Python3 requirements […]

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The source code of the bot that displays erotic images on Discord

このコードはDiscord.pyとNeko APIを使ったNsfw画像表示ボットのソースコードです。 成人向けコンテンツを含むボットなので、不快になる方はこのボットの作成中止をおすすめします。 まず、install.batを起動してください。 そのあとに、config.json を開き自分のボットのトークンを “token” の欄に入れます。 その下の “prefix” という欄には ボットのプレフィクスを入れてください。 そうしたら、 起動.bat を開いて、起動完了です! MITライセンス このソースコードを無制限で使用することができます。 商用、非商用、改変、再配布などのことも許可されます。 ですが、著作権表示と英語のライセンス全文を使用者が目に留まる場所に置いてください。 このソースコードを使用して発生したいかなる損害も一切責任を取りません。ご注意ください。 © Hanakuso2525 GitHub View Github    

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A simple and convenient build-and-run system for C and C++

Smake is a simple and convenient build-and-run system for C and C++ projects. Why make another build system? CMake and GNU Make are great build systems. However, as the project gets larger, and as there are increasingly many types of builds (e.g. a builds for debugging), it becomes tedious to add duplicate code. Smake solves this problem with its target-mode-build hierarchy. In this system,every project has a set of targets, and each target has a set of build modes. Whensmake […]

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Auto grind btdb2 exp for tower

Auto grind btdb2 exp for towers Setup I suggest checking out every screenshot to see what they are supposed to be, so you can screenshot the correct areas You need to change the coords of screenshots by commenting and running setup.py After changing the coords, you also need to change the respective coords in main.py You also need to run pip install requirements.txt GitHub View Github    

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A new data augmentation strategy utilizing the natural structural complexity of pictures such as fractals

Introduction In real-world applications of machine learning, reliable and safe systems must considermeasures of performance beyond standard test set accuracy. These other goalsinclude out-of-distribution (OOD) robustness, prediction consistency, resilience toadversaries, calibrated uncertainty estimates, and the ability to detect anomalousinputs. However, improving performance towards these goals is often a balancingact that today’s methods cannot achieve without sacrificing performance on othersafety axes. For instance, adversarial training improves adversarial robustnessbut sharply degrades other classifier performance metrics. Similarly, strong dataaugmentation and regularization techniques often […]

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