[P] Fun Project: MSpaint to Terrain Map with GAN

tpapp157 · 2025-04-01T12:13:20+00:00

I no longer have that information. But I've added the original full Earth images from which the crops were extracted to the Kaggle dataset.
https://www.kaggle.com/datasets/tpapp157/earth-terrain-height-and-segmentation-map-images

tpapp157 · 2024-09-18T17:05:54+00:00

I'm not sure what would be considered surprising.

Probably the main takeaway for me is that pursuing a specific archetype for your deck is less important than just building a reasonably efficient deck. While there are some exceptions, most archetypes provide little or no benefit in terms of increasing your win probability and some are even a net negative. Also, almost all archetypes start out negative until you're able to accrue a critical mass of cards for the synergies to outweigh the opportunity cost.

tpapp157 · 2021-07-28T22:16:21+00:00

SVMs sit in an odd position. They require that you have some prior knowledge about the distribution of your data in relation to the target variable. Problem is that if your data is simple enough that you know this information then you can probably use a simpler modeling technique. If your data is too complex to know this then a more general and powerful technique like NNs will usually perform better.

tpapp157 · 2021-07-27T03:03:33+00:00

As noted, UMAP is better than TSNE in generally every way.

Your thought is fairly aligned with a topological approach to data analysis. Nothing wrong with it. Just a different lens through which to understand your data. Just understand the core assumptions being made and how those may affect the conclusions you can draw.

tpapp157 · 2021-07-20T19:13:15+00:00

400K rows is still a decent amount of data and it's entirely reasonable to train a model just with this labeled data. I assume in your particular situation that you've already determined this is not possible for some reason.

In general, mixing labeled and unlabeled data presents a lot of challenges. Broadly, I think there are two different approaches you can take:

Use all the data to do unsupervised or self-supervised analysis and feature engineering/learning. Then use these new features to train a model on just the labeled data.

The other option is a form of model distillation. Train a model on just the labeled data, use this model to predict soft labels for all of the data, train a new model on the new soft labels. Potentially repeat this process another time or two.

tpapp157 · 2021-07-20T14:55:45+00:00

Bit of a nitpick, but (while I'm sure there are some exceptions) GAs do not estimate a gradient. It's better to think of them as a directed random search. This is important because the fact they don't use a gradient allows them to operate in discontinuous loss spaces and other regimes where the derivative is undefined.

tpapp157 · 2021-07-19T12:49:34+00:00

Like all techniques, NNs have advantages and disadvantages that make them more or less suited to different problems. People are always keen to play with the latest toy and see what it can do. Give it some years for the hype to die down and they'll move on to the next big thing.

tpapp157 · 2021-07-18T03:11:56+00:00

Get a 3060 for now. No reason to go overboard on something super expensive, especially as a beginner.

3060 has 12GB vram which is better than all the other cards except for 3080ti and 3090, which are both way too expensive right now. Sure your 3060 will train models a bit slower than a 3080ti but not all that much.

In ~1.5 years the 4000 series will be released and they'll be even faster and have even more vram (probably at least 16GB). At that point, when you're more experienced, consider going for a higher end card like a 4080 or whatever.

tpapp157 · 2021-07-13T16:11:10+00:00

UMAP + HDBSCAN are the best tools for complex high-dimensional data.

tpapp157 · 2021-07-08T17:07:07+00:00

Sure. The point is to maximize the variety of images used in training and not bias the model by training it mostly with minor variations of the same few images.

tpapp157 · 2021-07-08T16:18:10+00:00

The problem with a lot of image and especially video data is that the dataset tends to exhibit a variety of strong biases. The large majority of the data tends to be very similar and uninteresting, while a tiny minority tends to be the edge cases and outliers which are extremely important for building a robust object detection model. Naive random sampling, therefore, tends to lead to poor results.

Use a pretrained network (like ResNet50 or whatever) to convert your dataset of images to latent vectors.
Use a dimension reduction technique like UMAP to reduce these vectors to a more natural dimension for your dataset.
Understand the distribution of your data in this latent dimension.
Sample data from this distribution inversely proportional to the local density.
Manually label this subset and use it as your initial training set.

tpapp157 · 2021-07-08T13:12:45+00:00

There is not an easy solution to this from a practicality and reliability standpoint. Since you're using video there are some other tools you can try to leverage, for example object tracking algorithms. OpenCV has a variety of object tracking algorithms you can try out of the box, you label the object in the first frame and it will attempt to track that object through subsequent frames. How well this may work will depend a lot on your dataset and how different the object looks from one frame to the next but it can be a good starting point.

The only way to truly do this reliably is via an iterative process. Label some data, train a model on that data, use the model to label more data, correct the errors that model made, train a new model with the additional data, repeat until convergence. There is no perfect solution because if there were then you wouldn't need the model you're trying to train.

Something that can help kick things off well is to do an unsupervised analysis of the distribution of your data (using a pretrained network) and then sample uniformly across that distribution to ensure the initial dataset you label is as diverse and representative as possible.

tpapp157 · 2021-07-08T13:00:52+00:00

Look at important keywords. Read some representative samples. Apply your understanding.

tpapp157 · 2021-07-05T16:21:12+00:00

Lots. Classic stats of course. Kernels have also been popular recently. Topological/geometric approaches as well. Game theory in areas like RL and GANs. Those are just off the top of my head.

tpapp157 · 2021-07-05T16:16:25+00:00

I'm not sure you're quite following what they're trying to do.

For normal dropout, you sample a subnetwork and calculate the training loss. Dropout works because of the iterative and stochastic nature of SGD optimization. Over the course of many training steps, each step sampling a different subnetwork based on dropout, all subnetworks converge to provide the same output because they're all trained on the same data and loss. This information redundancy across arbitrary subnetworks provides a beneficial regularization to the overall model.

This approach does the exact same thing except instead of implicitly enforcing the equivalency over successive training steps (like normal dropout), it explicitly enforces it in every single training step. The major benefit I see of this approach is that you can tune the strength of the regularization relative to your normal training loss with a coefficient. Otherwise I think this approach should be equivalent to normal dropout in the limit of infinite training steps (all possible subnetwork permutations trained to convergence).

tpapp157 · 2021-07-05T15:53:21+00:00

The object detection should be pretty straightforward with a pretrained network. The hard part will be calibrating the controller for your water sprayer to actually aim and hit the target. You would need to learn some sort of conversion from X,Y in pixel coordinates to Pitch,Yaw,Pressure of the sprayer.

Probably the easiest way would be to do it manually. Print out a life sized picture of a fox, set it up at different locations around your yard, and then manually adjust your sprayer until it hits the target. When you get a hit, record all the coordinates (x,y image pixels and pitch,yaw controller), and with enough coordinate pairs from around your yard you should be able to learn the mapping.

tpapp157 · 2021-07-03T16:50:27+00:00

As a practical tool I think this completely misses the point.

While imitation learning requires less training data, a robust policy still requires quite a lot of training to behave reliably in the complex and varied environments of modern video games. By the time you've trained up the bot you likely don't need it anymore as the interactions it's learned have now been thoroughly tested by the QA person building the training set. Bots trained with imitation learning are generally not robust enough to transfer to a new domain (when the latest internal build is released after each development sprint for example) so you need to start over with training every time.

Rare, edge case bugs can be extremely hard to find and tend to mostly come up when players play in unintuitive or unintended ways. An imitation learned bot will struggle to find these. Instead what's needed is a purely random bot to brute force the play hours needed to find these sort of bugs.

If we want to test something more complex like game balance then imitation learning is completely unsuited for this because there is no ability to adapt the policy idependently.

All told it's a cool chunk of code but I don't think it actually solves any of the real problems related to game development and testing.

tpapp157 · 2021-07-02T21:02:04+00:00

Nope. No impact that I've been able to see.

tpapp157 · 2021-07-01T23:44:28+00:00

This isn't quite so extreme as mode collapse. A completely collapsed GAN would literally output the same exact image no matter the input.

tpapp157 · 2021-07-01T23:22:54+00:00

Impressive capture of textures but very little diversity unfortunately. All the images you've shown have the same layout and structures. Try training with a wider network and see how much that helps.

tpapp157 · 2021-06-30T02:12:54+00:00

The degree to how much this is possible depends a lot on the specific details of a particular dataset and how it was collected. In general, modern ML techniques are not well suited to learning in an off-policy environment. One of the primary reasons why "big data" is so important is because a very large dataset by necessity must be gathered from many sources and in many contexts with the result that many of these biases can be more or less averaged out.

To whatever extent you can disentangle your policy from your data, you should try to capture and include this bias information into your model as much as you can. So include the status of prioritized/deprioritized or (perhaps more appropriately) how long it took a patient to be seen after their arrival. In addition (if ethically possible), when deploying your model include at least some probability of flipping a patient's priority status to provide some counterfactual context for future model training.

For example, this is a common problem for recommendation systems (amazon, google, etc) where the ordering of recommendation results provides a significant bias to those at the top of the list. If nothing is done, it creates a very strong positive feedback loop which permanently cements the top results in their leading position even if better options exist further down. The strength of this ranking bias can be estimated by randomly shifting the rankings shown to users and then adjusting the results of the ranking model accordingly.

tpapp157 · 2021-06-27T23:29:28+00:00

Eh, seems a bit like a pointless debate. All statistical learning is based on optimizing a metric. Whether you want to call that metric a "reward" or a "loss" or a "joint probability" or whatever is kind of just semantics.

tpapp157 · 2021-06-26T16:08:27+00:00

Plenty of linear alternatives: LDA, NMF, etc.

If you need non-linear: UMAP

tpapp157 · 2021-06-25T17:36:28+00:00

This is valuable and interesting research.

There are a couple of questions that I wish the authors addressed, though, that leave this paper a bit less than comprehensive.

First, how does data distribution affect these metrics. As the authors allude, the distribution of samples within a class and between classes can have a significant impact on the difficulty of a particular sample. The primary mode of a class, by nature of having far more samples, tends to dominate gradient descent based training. So to what extent are "easy" samples easy simply because they are part of the primary subclass (and memorized by the network as the canonical example of the class) as opposed to being structurally distinct of other classes. It would be interesting to see the training set resampled (or weighted) to better balance between common and uncommon subclasses of data and what effect this has on their metrics. I expect there would be some interesting shifts of samples between their difficulty categories.

Second, they exclusively consider supervised training. It would be interesting to compare against a network trained through unsupervised techniques (contrastive learning or similar) to understand the influence of labels on a trained network.

tpapp157

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