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Research#llm📝 BlogAnalyzed: Dec 28, 2025 11:00

Beginner's GAN on FMNIST Produces Only Pants: Seeking Guidance

Published:Dec 28, 2025 10:30
1 min read
r/MachineLearning

Analysis

This Reddit post highlights a common challenge faced by beginners in GAN development: mode collapse. The user's GAN, trained on FMNIST, is only generating pants after several epochs, indicating a failure to capture the diversity of the dataset. The user's question about using one-hot encoded inputs is relevant, as it could potentially help the generator produce more varied outputs. However, other factors like network architecture, loss functions, and hyperparameter tuning also play crucial roles in GAN training and stability. The post underscores the difficulty of training GANs and the need for careful experimentation and debugging.
Reference

“"when it is trained on higher epochs it just makes pants, I am not getting how to make it give multiple things and not just pants."”

Permalinkr/MachineLearning
Research#llm🔬 ResearchAnalyzed: Jan 4, 2026 08:48

3One2: One-step Regression Plus One-step Diffusion for One-hot Modulation in Dual-path Video Snapshot Compressive Imaging

Published:Dec 19, 2025 13:44
1 min read
ArXiv

Analysis

This article presents a novel approach (3One2) for video snapshot compressive imaging. The method combines one-step regression and one-step diffusion techniques for one-hot modulation within a dual-path architecture. The focus is on improving the efficiency and performance of video reconstruction from compressed measurements.

Key Takeaways

    Reference

    “”

    PermalinkArXiv
    Research#llm📝 BlogAnalyzed: Jan 3, 2026 06:23

    Learning Word Embedding

    Published:Oct 15, 2017 00:00
    1 min read
    Lil'Log

    Analysis

    The article provides a concise introduction to word embeddings, specifically focusing on the need to convert text into numerical representations for machine learning. It highlights one-hot encoding as a basic method. The explanation is clear and suitable for a beginner audience.
    Reference

    “One of the simplest transformation approaches is to do a one-hot encoding in which each distinct word stands for one dimension of the resulting vector and a binary value indicates whether the word presents (1) or not (0).”

    PermalinkLil'Log