Research map / Probabilistic and scientific modeling / Variational and generative sampling
Fully Spiking Variational Autoencoder
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| replace ANN layers with the fully spiking FSVAE architecture | ANN VAE · MNIST | Inception Score: 5.947 -> 6.209 | Helped |
| replace ANN layers with the fully spiking FSVAE architecture | ANN VAE · Fashion MNIST | FID: 123.7 -> 90.12 | Helped |
| replace ANN layers with the fully spiking FSVAE architecture | ANN VAE · CIFAR10 | Autoencoder Fréchet Distance: 196.9 -> 133.9 | Helped |
| replace ANN layers with the fully spiking FSVAE architecture | ANN VAE · CelebA | FID: 92.53 -> 101.6 | Hurt |
| replace ANN layers with the fully spiking FSVAE architecture | ANN VAE · CelebA | Inception Score: 3.231 -> 3.697 | Helped |
| use MMD instead of KLD as the prior-posterior distance (ablation) | FSVAE · CelebA | FID: 114.3 -> 106.0 | Helped |
| add PSP to the MMD kernel (ablation) | FSVAE with MMD · CelebA | FID: 106.0 -> 101.6 | Helped |
Closest papers by meaning
- From Variational to Deterministic Autoencoders (ICLR 2020 · similarity 0.89)
- Diagnosing and Enhancing VAE Models (ICLR 2019 · similarity 0.87)
- EnOF-SNN: Training Accurate Spiking Neural Networks via Enhancing the Output Feature (NeurIPS 2024 · similarity 0.87)
- Poisson Variational Autoencoder (NeurIPS 2024 · similarity 0.87)
- Enhancing Training of Spiking Neural Network with Stochastic Latency (AAAI 2024 · similarity 0.86)
- S$^2$NN: Sub-bit Spiking Neural Networks (NeurIPS 2025 · similarity 0.86)
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