- A flow's prior is typically fixed (e.g. N(0, I)). We learn it and use a lightweight flow to model pixel dependencies;
- This makes sampling (ODE solving) more efficient, without sacrificing performance in our setting;
- We introduce bespoke training objectives for both autoregressive and continuous-time flow variants;
- Flow-SSN achieves SOTA performance on standard stochastic segmentation benchmarks!
- A flow's prior is typically fixed (e.g. N(0, I)). We learn it and use a lightweight flow to model pixel dependencies;
- This makes sampling (ODE solving) more efficient, without sacrificing performance in our setting;
- We introduce bespoke training objectives for both autoregressive and continuous-time flow variants;
- Flow-SSN achieves SOTA performance on standard stochastic segmentation benchmarks!