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A rough roadmap of things to be done for UMAP. Some of these tasks are easy, some are hard, and some require deeper knowledge of UMAP. Short and medium term tasks should be approachable for many peopl…
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https://arxiv.org/pdf/1702.05464.pdf
Adversarial learning methods are a promising approach to training robust deep networks, and can generate complex samples across diverse domains. They also can i…
leo-p updated
7 years ago
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### Prize category
Best Content
### Overview
Impact of GenAI and LLMs on our Environment
Our project focuses on importance of evaluating the environmental footprint of Large Language Mod…
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I use your keras example in README.md. And get only 0.9 acc in MNIST, which is equal to random guess.
What's the best way to train the plain MNIST classifier using SimNet?
Could you give more de…
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Is it possible to access the output of inner network layers using this codebase and, if so, how?
I ask because we’re interested in 1) training a network on certain kinds of audio and then using the…
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Hi, Following the provided instructions, I've created a [gitpod](https://github.com/bitsnaps/generative-models/tree/gitpod) branch to automate these instructions, I keep getting this error even after …
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Hi, a few of us at UCL CS (and further afield) have a WIP submission; I realize we're getting down to the wire and we're having a bit of a challenge finishing this this off so I wanted to share our pr…
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It seems that the authors of this paper no longer maintain their code.
Could anyone share an updated version of this code?
It will help us a lot. Thanks!!!
ylmao updated
5 years ago
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# Abstract
Generative Adversarial Networks(GAN)은 데이터 생성에서 뛰어난 모습을 보이고 있다. 많은 영역에서 쓰이고 있지만 여전히 안정적인 학습에는 어려움이 따른다. 문제점으로는 Nash-equilibrium, internal covariate shift, mode collapse, vanishing gradient,…
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* Mutual Information Boosted Precipitation Nowcasting from Radar Images
* StylerDALLE: Language-Guided Style Transfer Using a Vector-Quantized Tokenizer of a Large-Scale Generative Model