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Show HN: My Project on Generative Adversarial Networks(github.com)

35 points by mlwhiz 6 months ago flag hide 11 comments

generative_ai 6 months ago next
Great work on the generative adversarial networks project! I've been playing around with GANs myself lately and I'm impressed by how far they've come. Looking forward to seeing how you plan to apply this in the real world.
- user2 6 months ago next
  Thanks! I've always been fascinated by generative models and I think GANs have a lot of potential. I'm currently exploring applications in computer vision and natural language processing.
- ml_enthusiast 6 months ago prev next
  Have you tried using GANs for image-to-image translation? I feel like that's a really interesting area of research right now and there's a lot of exciting work being done on high-quality image generation.
- relative_newbie 6 months ago prev next
  Impressive work, I am just starting out with AI/ML, could you suggest any resources/packages that would be good to start with GANs in Python? I'm particularly interested in learning more about what's under the hood.
  generative_ai 6 months ago next
  I'm glad you found my project interesting! I suggest checking out Keras' GAN implementation in their repository as a starting point. It's a great resource for beginners with good docs to understand the layers involved. Additionally, here's a nice tutorial that you might find helpful: (Link to tutorial) Happy learning!
data_scientist 6 months ago prev next
I like the creativity with your work. Have you experimented with any unsupervised learning techniques? I think that's an exciting space to watch in the context of GANs for unstructured data.
- generative_ai 6 months ago next
  Yes, unsupervised learning is a really interesting area to apply GANs. I've worked on using them for clustering and dimensionality reduction tasks but I'd like to explore the space more. I particularly liked this paper on using GANs for unsupervised learning: (Link to paper) Feel free to check it out!
deep_learning 6 months ago prev next
Great work on GANs! I've also been exploring architectures that use spectral normalization to stabilize GAN training like the ones mentioned in the paper - Improved Training of Wasserstein GANs. Have you tried this yet and if so, what is your experience?
- generative_ai 6 months ago next
  I agree, spectral normalization is a great way to ensure that GANs converge well. I have tried out architectures involving spectral normalization and it reduces both exploding as well as vanishing gradients. But the trade-off is that it takes longer to train because you have to maintain spectral norm of the weights of each layer.
ai_learner 6 months ago prev next
Inspiring work! I have just started with GANs and I wanted to ask how can I ensure that my dataset is balanced when working with GANs? I'm worried that unbalanced data may affect the results.
- generative_ai 6 months ago next
  Good question, balancing the datasets is crucial for GANs to work well. You can balance the classes by up-sampling under-presented classes and down-sampling majority classes. I recommend using the following heuristics: 1) Use data-augmentation to increase the size of the minority class. 2) Create synthetic examples for under-represented classes using GANs/VAEs. I wrote a blog post on this here: (Link to blog post)It should help you with the data balancing act!

generative_ai 6 months ago next
Great work on the generative adversarial networks project! I've been playing around with GANs myself lately and I'm impressed by how far they've come. Looking forward to seeing how you plan to apply this in the real world.
- user2 6 months ago next
  Thanks! I've always been fascinated by generative models and I think GANs have a lot of potential. I'm currently exploring applications in computer vision and natural language processing.
- ml_enthusiast 6 months ago prev next
  Have you tried using GANs for image-to-image translation? I feel like that's a really interesting area of research right now and there's a lot of exciting work being done on high-quality image generation.
- relative_newbie 6 months ago prev next
  Impressive work, I am just starting out with AI/ML, could you suggest any resources/packages that would be good to start with GANs in Python? I'm particularly interested in learning more about what's under the hood.
  generative_ai 6 months ago next
  I'm glad you found my project interesting! I suggest checking out Keras' GAN implementation in their repository as a starting point. It's a great resource for beginners with good docs to understand the layers involved. Additionally, here's a nice tutorial that you might find helpful: (Link to tutorial) Happy learning!
data_scientist 6 months ago prev next
I like the creativity with your work. Have you experimented with any unsupervised learning techniques? I think that's an exciting space to watch in the context of GANs for unstructured data.
- generative_ai 6 months ago next
  Yes, unsupervised learning is a really interesting area to apply GANs. I've worked on using them for clustering and dimensionality reduction tasks but I'd like to explore the space more. I particularly liked this paper on using GANs for unsupervised learning: (Link to paper) Feel free to check it out!
deep_learning 6 months ago prev next
Great work on GANs! I've also been exploring architectures that use spectral normalization to stabilize GAN training like the ones mentioned in the paper - Improved Training of Wasserstein GANs. Have you tried this yet and if so, what is your experience?
- generative_ai 6 months ago next
  I agree, spectral normalization is a great way to ensure that GANs converge well. I have tried out architectures involving spectral normalization and it reduces both exploding as well as vanishing gradients. But the trade-off is that it takes longer to train because you have to maintain spectral norm of the weights of each layer.
ai_learner 6 months ago prev next
Inspiring work! I have just started with GANs and I wanted to ask how can I ensure that my dataset is balanced when working with GANs? I'm worried that unbalanced data may affect the results.
- generative_ai 6 months ago next
  Good question, balancing the datasets is crucial for GANs to work well. You can balance the classes by up-sampling under-presented classes and down-sampling majority classes. I recommend using the following heuristics: 1) Use data-augmentation to increase the size of the minority class. 2) Create synthetic examples for under-represented classes using GANs/VAEs. I wrote a blog post on this here: (Link to blog post)It should help you with the data balancing act!