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Questions tagged [generative-model]

For questions related to the concept of generative machine learning models, such as the Restricted Boltzmann Machine (RBM), the Variational Autoencoder (VAE), the Generative Adversarial Network (GAN), Large Language Models (LLMs), and Diffusion Models.

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How do Energy Based Models solve Multiple possible outputs given one input

I've been looking into Energy Based Models recently which Yann LeCun has been strongly advocating for. One problem that he lists with probabilistic based models is that in the case when there are ...
Kiran Manicka's user avatar
1 vote
1 answer
134 views

Trying to understand some derivation in the paper: Deep Unsupervised Learning using Nonequilibrium Thermodynamics

I have recently been learning about diffusion models and trying to derive all the results in the paper by Sohl-Dickstein, et. al, "Deep Unsupervised Learning using Nonequilibrium Thermodynamics&...
ahxmeds's user avatar
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32 views

What type of AI model should I use to generate practice questions?

I have a set of multiple choice English questions, and I wanted to use AI to generate more questions to quiz myself with. I know there are platforms online to accomplish this, but I wanted to ...
vp27's user avatar
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3 votes
0 answers
47 views

How random should an untrained generative AI output really be?

I am developing a particular implementation of VAE, and, how usually one does while implementing any architecture, I passed a random input to the model to test if everything worked fine (e.g. check ...
GPU'njoyer's user avatar
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1 answer
103 views

What does it mean to "learn a distribution", and what does it contain?

When I was reading about discriminative vs generative models, I came across their definitions: Given a distribution of inputs $X$ and labels $Y:$ Discriminative models learn the conditional ...
abcd's user avatar
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4 votes
1 answer
45 views

Likelihood function for Gaussian Discriminant Analsis

Im trying to understand how the likelhood function for gaussian discriminant analysis is derived. I self studying Murphy's Probabilistic Machine learning, and in it, he states the likelihood function ...
turtle_in_mind's user avatar
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0 answers
45 views

Why do mix models work?

Is there research on why models mixes work? One would expect that averaging the weights of two models would produce garbage, but many models mixes created by amateurs show that they not only work, but ...
allo's user avatar
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1 vote
0 answers
22 views

Implications of Not Zero-Centering Data in Diffusion Models: Impact on Performance and Stability?

I've been exploring the implementation and theory behind diffusion models, particularly in how they manage the latent space. A recurring theme is the emphasis on zero-centering the latent space and ...
Voyager's user avatar
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1 vote
0 answers
66 views

Are diffusion models still beneficial in highly compressed latent spaces?

Consider for example the MNIST dataset. When we apply diffusion to the pixel space, the image slowly becomes more and more noisy until white noise has been reached (like below). In the last step (t=...
Thomas Wagenaar's user avatar
0 votes
1 answer
64 views

Main subjects to learn Artificial Intelligence in CS [closed]

In my PhD, I will work with ML models. However, I will only use ready-made models as a tool, but I want to delve deeper into Artificial Intelligence not just to use ready-made models, but to ...
Everson Gomes's user avatar
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0 answers
15 views

Which AI model was used to create this video?

I just stumbled upon this hilarious youtube short of Harry potter and his friends having a rave. I'd like to know which AI model was most likely used to create these images. Does anyone know?
Maurice's user avatar
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10 votes
3 answers
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Why are LLMs able to reproduce bodies of known text exactly?

Mathematically, I wouldn't expect LLMs to be able to reproduce source texts exactly unless the source text was the probable outcome given some prompt. However, I have now tested HuggingFaceH4/zephyr-...
Grant Curell's user avatar
1 vote
1 answer
38 views

Comparing the performances of GPTs with deep learning in the field of binary files and their related reports

Regarding the case study of a dataset including binary files (containing assembly code) and reports related to each file (the content of the static analysis of the file as well as the analysis of the ...
user16385455's user avatar
1 vote
2 answers
972 views

Do diffusion models take a long time to train?

I am trying to train a diffusion model (from scratch in pytorch). UNet used is not anything too fancy, takes in images and time step as input for about 512 time steps. I am using learnable embeddings ...
Aditya 's user avatar
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0 answers
54 views

DDPM - why after adding gaussian noise to image, we assume that new image is from normal distribution?

I have a question about forward process in DDPM. It is described as we sample our image from some distribution: $x_0\sim{q(x)}$ then in each time stamp $T$ we are applying gaussian noise $\epsilon\sim\...
cvzx's user avatar
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0 votes
1 answer
44 views

If the unigram precision is (N-1)/N, then the bigram precision is :

Consider the following machine translation scenario. The reference translation has N words (do not consider sentence beginner ‘hat’ and sentence finisher ‘dot’). The machine output also has N words. ...
Geeklovenerds's user avatar
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0 answers
37 views

Can higher order derivatives be used in diffusion models?

I remember reading an article about predicting both the first and second derivative in a diffusion model and then using the Taylor method to denoise in each step, to achieve faster convergence. But I ...
Sartem Cacartem's user avatar
1 vote
0 answers
75 views

Pointers to (deep) latent variable models that admit analytical approximations

I am aware that there is a plethora of deep generative models out there (e.g. variational autoencoders (VAE), GANs) that can model high-dimensional data as the images of latent variables under a non-...
ngiann's user avatar
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0 votes
0 answers
41 views

VAE for Motion Sequence Generation - Convergence Issue with Scheduled Sampling

I have implemented a Variational Autoencoder (VAE) in PyTorch for motion sequence generation using human pose data (joint angles and angular velocities in radians) from the CMU dataset. The VAE ...
RTn's user avatar
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1 vote
0 answers
69 views

How does diffusion model (DDPM) ensures novel generated samples?

I am trying to understand the theoretical aspect of the denoising diffusion model. There we try to destroy the initial image x_0 through a chain of forward process and then learn a backward diffusion ...
Formal_this's user avatar
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0 answers
22 views

Training Diffusion Probabilistic Models

My question is in regards to the training process for Diffusion Probabilistic Models by Sohl-Dickenstein et. al. and also Ho et. al. and this blog post For the derivation of the model log-likelihood ...
diffusionQuestion's user avatar
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0 answers
9 views

What is the detailed experimental setup for class-incremental continual image generation?

Do you condition the generative model (let's say, VAE) on the task identity or the class label or both? If I condition the VAE on both task identity and class label, then I have to provide both the ...
Homie98's user avatar
1 vote
1 answer
163 views

What does "fully visible belief network" stand for?

In CS231n, I saw the terminology "fully visible belief network", one category of explicit density generative model such as PixelCNN and NADE. Although I can understand what this terminology ...
Chris's user avatar
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0 votes
0 answers
20 views

Where to find all the documentation regarding StableDiffusion's special prompts (like mixing two persons)

I am currently playing on ClipDrop.co with Stable Diffusion, and I am confused with the tutorials I can find online. For instance, for mixing two celebrities in one pics, I read all the following ...
Myoch's user avatar
  • 101
0 votes
0 answers
53 views

How to generate quality synthetic images of human subjects to be used for training stable diffusion

I'm trying to generate some synthetic images of humans, that are "good enough" to be used to fine-tune a stable diffusion model, but i'm not sure if this is possible. I have experimented ...
interesting's user avatar
1 vote
1 answer
100 views

What is x, y, p(x), p(y) in generative model domain?

Background Generative modeling Generative modeling aims to model the probability of observing an observation x. $$ p(x) = \frac{p(y\cap x)}{p(y|x)} $$ ...
Prakhar's user avatar
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0 votes
1 answer
194 views

Justification of Scaling in Classifier-Free Guidance in Diffusion Models

Background | Classifier-Free Guidance Derivation To summarize the derivation of Classifier-Free Guidance, looking at this paper (Page 21.), we can write classifier guidance as: $$\nabla_{x}\log p\...
snatchysquid's user avatar
0 votes
0 answers
21 views

Small language model vocabulary correction

I plan to train a small language model (less than 4B parameters) that can run on x86 and handle vocabulary correction such as: ...
Jeff Brower's user avatar
2 votes
1 answer
115 views

how the GAN architecture maintain similar images close in the latent space?

I am learning about generative models, and I don't quite understand how the GAN architecture can maintain similar generated images close in the latent space. For example, an autoencoder and a ...
Cesar Ruiz's user avatar
1 vote
2 answers
99 views

Maximize a scoring function within the latent space of a generative model

Given a generative model, G, trained on a dataset D. This generative model can be either GAN or Diffusion based. Supposed each sample, x_i, generated by G, can be evaluated by a readily available ...
terenceflow's user avatar
1 vote
0 answers
39 views

Can back-bone of text-to-image GEN AI models utilised for classification?

With the advent of GEN AI (Stable Diffusion), we are able to create images with text. For eg. If i need to create a dog on beach during sunset; now in background this model needs to first get images ...
prat__'s user avatar
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0 votes
1 answer
22 views

Can I sample finite or infinite images with AutoRegressive Models?

I'm learning about AutoRegressive Models used on images, and I've studied the training phase, where you model each pixel on the basis of the previous ones using a certain model architecture. My ...
SuperFluo's user avatar
0 votes
0 answers
28 views

Can AI-written text detection be made more accurate if you know the prompt?

Usually, genAI detection is of the form: Input: text. Output: was it generated by AI? Thus far, AI-written text detection is terribly inaccurate, and if you're a user of r/ChatGPT, you've probably ...
Rebecca J. Stones's user avatar
1 vote
1 answer
144 views

Is there a limitation to the amount of data that a genAI model could be trained upon?

My friend says that genAI would become more human like, and perhaps even smarter than humans if it were simply trained on more and more data. I say that this would overtrain the models, and we would ...
tryst with freedom's user avatar
2 votes
1 answer
108 views

Could hallucinations be the demise of the AI hype? [closed]

For quite some time now, I have been evaluating ChatGPT's capability to deliver accurate and helpful responses. While its performance is undeniably impressive, the issue of hallucinations poses a ...
machine_1's user avatar
  • 133
3 votes
1 answer
480 views

What's the architecture that allows the generation of new images based on input image in tools like Midjourney?

I understand that the high-level architecture of tools like Midjourney use diffusion models to generate images from text. What I don't understand is which type of network architecture allow the second ...
emilaz's user avatar
  • 133
0 votes
0 answers
32 views

KL divergence and sign [duplicate]

In the Auto-Encoding Variational Bayes paper, the formula for KL divergence is $$ \frac{1}{2} \sum \bigl (1 + \log(σ^2) - μ^2 - σ^2 \bigr) \space\space\space\space...(10)$$ , but the equation is $$- ...
diffusion stable's user avatar
0 votes
0 answers
44 views

How far is AI from making movies just by using script or description like we use for generating images in midjourney or DALL-E etc

I'm curious about the current state of AI technology when it comes to generating movies from textual descriptions. I'm aware of impressive advancements in generating images using models like ...
طلحة's user avatar
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2 votes
2 answers
3k views

About cosine noise schedule in Diffusion Model

Could you provide the explanation of Figure 4 from the paper at Improved Denoising Diffusion Probabilistic Models? (1) The paper says, "the end of the forward noising process is too noisy,and so ...
diffusion stable's user avatar
0 votes
1 answer
154 views

Could generative AI bootstrap?

In programming, if a new language could be improved by the language itself, it’s call self-hosting or bootstrapping. To develop generative AI, there’s some steps, data preparing, model training, fine ...
zzzgoo's user avatar
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0 votes
0 answers
178 views

Diffusion Models - Loss normalization for different noise schedules

With reference to the paper "Denoising Diffusion Probabilistic Models", and running the code given in this notebook showing a simple example on "flattened" data, I have the ...
Peblo's user avatar
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0 votes
0 answers
158 views

Resources to understand the math behind diffusion models

I started learning about diffusion models, but I couldn't follow the math proofs (probability distribution terms and other stuff), is there any sources you suggest to understand what they're talking ...
hakim47's user avatar
0 votes
0 answers
14 views

What's the right approach to "teach" a chatbot about a big specific data set: training it or feeding it the input once?

I would like to build a chatbot that I can talk to about any specific data set I provide and I have a theoretical question. Let's say, I want to discuss French cuisine. This could involve providing ...
lesssugar's user avatar
  • 101
3 votes
1 answer
87 views

Clarification on the training objective of denoising diffusion models

I'm reading the Denoising Diffusion Probabilistic Models paper (Ho et al. 2020). And I am puzzled about the training objective. I understood (I think) the trick regarding the reparametrization of the ...
user3903647's user avatar
0 votes
1 answer
137 views

Blurring of image in generative model using diffusion probabilistic method

In the 2015 paper "Deep Unsupervised Learning using Nonequilibrium Thermodynamics" by Sohl-Dickstein et al. on diffusion for generative models, Figure 1 shows the forward trajectory for a 2-...
sunfishstanford's user avatar
3 votes
0 answers
228 views

Reverse Process in Diffusion Model Doesn't Return Original Image

I am attempting to program a Denoising Diffusion Model based on the one introduced in the article by Ho et al. (2020). However, I have run into issues while testing the reverse diffusion process. ...
cabralpinto's user avatar
0 votes
1 answer
137 views

Keypoint generation in 3D point clouds with Deep Learning

I have a huge dataset of 3D point clouds (each point consists of X,Y,Z coordinates) and another dataset with keypoints (also X,Y,Z) which lie on quite recognizable structures in the point cloud. As a ...
nmb's user avatar
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1 vote
0 answers
89 views

Coding a conversational AI which remembers previous context

I am trying to code a proper conversational AI which remembers previous context and answers accordingly (something like a micro ChatGPT). Additionally I want the AI to work on a custom knowledge base ...
JAYDEEP GHOSE's user avatar
2 votes
2 answers
2k views

What makes ChatGPT a generative model?

I'm working my way through how ChatGPT works. So I read that ChatGPT is a generative model. When searching for generative models, I found two defintions: A generative model includes the distribution ...
Ai4l2s's user avatar
  • 121
1 vote
1 answer
177 views

What is the difference between the term "generative" in classical machine learning and deep learning?

There are lots of explanations on DGM (Deep Generative Model) and generative classifier (most of the explanations on which are about generative classifier vs discriminative classifier) But, I can ...
Rhee's user avatar
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