All Questions
189 questions with no upvoted or accepted answers
6
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Are generative models actually used in practice for industrial drug design?
I just finished reading this paper MoFlow: An Invertible Flow Model for Generating Molecular Graphs.
The paper, which is about generating molecular graphs with certain chemical properties improved the ...
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5
votes
1
answer
2k
views
Wasserstein GAN: Implemention of Critic Loss Correct?
The WGAN paper concretely proposes Algorithm 1 (cf. page 8). Now, they also state what their loss for the critic and the generator is.
When implementing the critic loss (so lines 5 and 6 of Algorithm ...
- 171
5
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3
answers
1k
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What's the difference between architectures and backbones?
In the paper "ForestNet: Classifying Drivers of Deforestation in Indonesia using Deep Learning on Satellite Imagery", the authors talk about using:
Feature Pyramid Networks (as the ...
4
votes
0
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450
views
GAN : Why does a perfect discriminator mean no gradient for the generator?
In the training of a Generative Adversarial Networks (GAN) system, a perfect discriminator (D) is one which outputs 1 ("true image") for all images of the training dataset and 0 ("false ...
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4
votes
1
answer
255
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Is there any relation between the recursive neural network and recurrent neural network?
Recurrent neural networks, abbreviated as RNNs, are widely used in deep learning literature, especially for text processing.
Are they related to recursive neural networks in any way?
I am asking for ...
- 3,990
4
votes
0
answers
61
views
What is the difference between "out-of-distribution (generalisation)" and "(meta)-transfer learning"?
I'm trying to develop a better understanding of the concept of "out-of-distribution" (generalization) in the context of Bengio's "Moving from System 1 DL to System 2 DL" and the concept of "(meta)-...
4
votes
1
answer
617
views
What are the differences between Bytenet and Wavenet?
I recently read Bytenet and Wavenet and I was curious why the first model is not as popular as the second. From my understanding, Bytenet can be seen as a seq2seq model where the encoder and the ...
- 1,158
4
votes
0
answers
614
views
What is the difference between GAT and GaAN?
I was looking at two papers
Graph Attention Networks (GAT) by Petar Veličković and
GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphs by Jiani Zhang.
I'm trying to ...
- 1,158
3
votes
0
answers
61
views
Best Machine Learning Model for "Predicted" Image Generation
I am currently working on undergraduate research to determine hotspots for hand-surface contact. Ideally, I would like to give the model a depth image as input:
Example of synthetic depth image
and ...
- 31
3
votes
0
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190
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Is the GAN architecture better suited for medical image denoising than the CNN?
I'm considering using GANs for medical image denoising, based on previous literature, like this and this. My input to the GAN would be a high-noise image and my ideal output would be a low-noise, high-...
- 31
3
votes
1
answer
634
views
What's the difference between domain randomization and domain adaptation?
In my understanding, domain randomization is one method of diversifying the dataset to achieve a better shot at domain adaptation. Am I wrong?
- 131
3
votes
0
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201
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What is the difference between fuzzy neural networks and adaptive neuro fuzzy inference systems?
I have, like you see, just a general question about the combination of fuzziness and neural networks. I understood it as follows
Fuzzy neural networks as a hybrid system: the neural network helps me ...
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3
votes
0
answers
43
views
How do reinforcement learning and collaborative learning overlap?
How do reinforcement learning and collaborative learning overlap? What are the differences and similarities between these fields?
I feel like the results I get via google do not make the distinction ...
- 297
3
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0
answers
133
views
Are No Free Lunch theorem and Universal Approximation theorem contradictory in the context of neural networks?
To my understanding NFL states that, we cannot have an hypothesis (let's assume it is an approximator like NN in this case) class that can't achieve certain accuracy parameters $\leq \epsilon$ with ...
user9947
3
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0
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78
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Can the importance sampling estimator have a non-stationary behaviour policy even if the target policy is stationary?
The inverse propensity score (IPS) estimator, which is used for off-policy evaluation in a contextual bandit problem, is well explained in the paper Doubly Robust Policy Evaluation and Optimization.
...
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3
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0
answers
1k
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How does deepfake technology work with multiple people in a single frame?
I was watching this video from corridor crew, according to them, they have used deepfake technology to create this video. I myself have never made a deepfake videos, but I have enough knowledge in the ...
- 1,096
3
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0
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18
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How does InfoGAN learn latent categorical codes on MNIST
While reading the InfoGAN paper and implement it taking help from a previous implementation, I'm having some difficulty understanding how it learns the discrete categorical code when trained on MNIST.
...
3
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0
answers
220
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What is the difference between Squeeze-and-excite and bottleneck modules from Mobilenet v2?
Squezee-and-excite networks introduced SE blocks, while MobileNet v2 introduced linear bottlenecks.
What is the effective difference between these two concepts?
Is it only implementation (depth-wise ...
- 101
3
votes
0
answers
366
views
What are the differences between CRF and HMM?
What I know about CRF is that they are discriminative models, while HMM are generative models, but, in the inference method, both use the same algorithm, that is, the Viterbi algorithm, and forward ...
- 161
3
votes
0
answers
45
views
How do GANs create an image with specific characteristics?
I've seen GANs that do things like convert an image to a painting or this GAN here https://make.girls.moe/#/ that takes in a set of characteristics and generates a waifu with those characteristics.
...
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3
votes
0
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310
views
What are the key differences between cellular neural network and convolutional neural network?
What are the key differences between cellular neural networks and convolutional neural networks in terms of working principle, implementation, potential performance, and applicability?
3
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0
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231
views
What is the relation between the definition of learnability of Vapnik and Gold and learnability of neural networks?
Gold showed that a language can be learned only if it contains a finite set of sentences.
We know that deep neural networks can implement any function. Does this contradict the Gold's result?
What ...
3
votes
0
answers
49
views
Is there a general adversarial network that can take multiple low quality images to create a higher quality image?
Is there a general adversarial network that can take multiple low-quality images of a subject to create a higher quality image of the subject?
SRGAN just takes a single low res image and makes it high ...
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2
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0
answers
246
views
cGAN: Discriminator loss going to zero while Generator's going always up but the result is very good
I have a Conditional Generative Adversarial Network for Quantum State Tomography. The metrics I am monitoring during the training process are the losses and the Fidelity (the degree of similarity ...
- 23
2
votes
0
answers
790
views
What is the difference between a diffusion model and GANs?
Recently, I hear a lot of people claiming that diffusion models beat GANs, also providing less training time. I've searched about these two type of models, and I am confused, because somehow they both ...
- 21
2
votes
0
answers
112
views
When are traditional image processing methods preferable to machine learning and why?
By traditional image processing I understand, e. g. using filters to improve the image, extracting edges and then classifying objects using template matching.
My current decision criteria are:
large ...
- 21
2
votes
0
answers
61
views
What are the specific differences between vision transformers variants?
I have tried 4 different types of attacks on vision transformers (ViT small and tiny, DeiT small and tiny) but the attack successes on smaller versions are higher than the tiny versions. My ...
- 31
2
votes
0
answers
28
views
What are the benefits of using spectral k-means over simple k-means?
I have understood why k-means can get stuck in local minima.
Now, I am curious to know how the spectral k-means helps to avoid this local minima problem.
According to this paper A tutorial on Spectral,...
- 121
2
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0
answers
119
views
When to model decision-making problem as single agent vs multi-agent problem?
I understand the goals and purposes of RL in the case of a single agent and the underlying model, i.e. MDPs, for RL problems (or sequential decision making with uncertainty in general).
My question is ...
- 121
2
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1
answer
74
views
Can teacher forcing in RNN ensure Turing completeness?
RNN has the same capability as a universal Turing machine. But I am confused whether RNN holds the same capabilities if we use teacher forcing.
Consider the following excerpts from paragraphs taken ...
- 3,990
2
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0
answers
153
views
What is the difference between Probabilistic Graphical models and Graph Neural networks?
While going over PGMs and GNNs, it seems like both leverage the graph data structure. The former has been used to represent causal associations (among other things), while the latter has a varied set ...
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2
votes
1
answer
173
views
Closed networks vs Networks with a removed delay to predict new data
I've come across two types of neural networks to predict, both from Matlab, the closed structure and the net that removes one delay to find new data.
From Matlab's app generated scripts we see:
% ...
2
votes
1
answer
860
views
Is there any difference between "image generation" and "image synthesis"?
Generative Adversarial networks (aka GANs) are used for image generation. The phrase image synthesis is also used in literature.
I know that the phrase image generation stands for
An act of ...
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2
votes
0
answers
94
views
Do the terms multi-task and multi-output refer to the same thing in the context of deep learning?
Do the terms multi-task and multi-output refer to the same thing in the context of deep learning (with neural networks)? For example, do neural networks for multi-task learning use multiple outputs?
...
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2
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0
answers
45
views
How does the output distribution of a GAN change if the parameters are slightly purturbed?
Suppose $G_{\phi}:\mathcal{Z}\rightarrow \mathcal{X}$ is a generator (neural network, non-invertible) that can sample from some distribution $\pi$ on $\mathcal{X}$. That is, $G_{\phi}(z)\sim \pi$ when ...
- 51
2
votes
1
answer
111
views
Classifying generated samples with Wasserstein-GAN as real or fake
I'm quite new to GANs and I am trying to use a Wasserstein GAN as an augmentation technique. I found this article
https://www.sciencedirect.com/science/article/pii/S2095809918301127,
and would like to ...
- 21
2
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0
answers
72
views
How and why do state-of-the-art models in medical segmentation differ from general segmentation models?
I am just getting into medical image segmentation and have been able to understand the state-of-the-art architectures, like Double UNet, UNet++, and Multiresunet.
What I haven't understood yet: Why ...
- 635
2
votes
0
answers
118
views
Generating fake faces containing specific features with GANs
I'm trying to understand how DeepFakes are generated and so far I understood that they're mostly generated through the usage of GANs and autoencoders.
The autoencoders part is understandable, but what ...
- 21
2
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0
answers
103
views
What is the difference between text-based image retrieval and natural language object retrieval?
I'm working on creating a model that locates the object in the scene (2D image or 3D scene) using a natural language query. I came across this paper on natural language object retrieval, which ...
- 21
2
votes
0
answers
515
views
What is the purpose of the DAMSM loss for the generators in AttnGAN?
I am confused about the training part in AttnGan.
If you observe page 3. There are two types of losses for generator network: one involving the Deep Attentional
Multimodal Similarity Model (DAMSM) ...
- 3,990
2
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181
views
Is the generator distribution in GAN's continuous or discrete?
I have some trouble with the probability densities described in the original paper. My question is based on Goodfellow's paper and tutorial, respectively: Generative Adversarial Networks and NIPS ...
- 21
2
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0
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99
views
How are the classical MDP and the object-oriented MDP views different?
I've been reading the attached paper - which aims to model entities in the world as objects, including the learning agent itself!
To say the least, the goal is to navigate through what seems like a ...
- 1,171
2
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0
answers
132
views
What is the relationship between PAC learning and classic parameter estimation theorems?
What are the differences and similarities between PAC learning and classic parameter estimation theorems (e.g. consistency results when estimating parameters, e.g. with MLE)?
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2
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0
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90
views
How should I design a reward function for a NLP problem where two models interoperate?
I would like to design a reward function. I am training two models from the first model that classify set of texts (paragraphs and keywords) and I also got some hidden states. The second model is ...
- 21
2
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0
answers
89
views
What is the difference between training a model with RGB images and using only the color channels separately?
What is the difference between training a model with RGB images and using only the color channels separately (like only the red channel, green channel, etc.)? Would the model also learn patterns ...
- 175
2
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0
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242
views
Why does GAN loss converge to log(2) and not -log(2)?
In Goodfellow's paper, he says:
Hence, by inspecting Eq. 4 at $D^*_G (\mathbf{x}) = \frac{1}{2}$, we find $C(G) = \log \frac{1}{2}+ \log \frac{1}{2} = − \log 4$. To see that this is the best ...
2
votes
0
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262
views
What are the advantages and disadvantages of extrinsic and perplexity model evaluation in NLP?
In the video Evaluation and Perplexity by Dan Jurafsky, the author talks about extrinsic and perplexity evaluation in the context of natural language processing (NLP).
What are the advantages and ...
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2
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0
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299
views
What is the difference between using a backbone architecture and transfer learning?
I'm super new to deep learning and computer vision, so this question may sound dumb.
In this link (https://github.com/GeorgeSeif/Semantic-Segmentation-Suite), there are pre-trained models (e.g., ...
- 21
2
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0
answers
48
views
How can I compare EEG data with accelerometer data in 1 algorithm?
I have frequency EEG data from fall and non-fall events and I am trying to incorporate it with accelerometer data that was collected at the same time. One approach is, of course, to use two separate ...
- 21
2
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0
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99
views
What is the difference between tracking and mapping (TAM) and localization and mapping (LAM)?
In the paper Visual SLAM algorithms: a survey from 2010 to 2016 by Takafumi Taketomi, Hideaki Uchiyama and Sei Ikeda it is mentioned
It should be noted that tracking and mapping (TAM) is used instead ...
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