WHAT I AM LEARNING

[dalessioluca]

0. Informative latent space (with clusters) is antithetical with a generator which works taking N(0,1) which needs structureless latent-space.

1. sigma should be chosen so that reconstruction is of order 1 (and therefore balanced with the rest of the term). A simple way to do it is: sigma2 = (x-x.mean()).pow(2).mean()

2. At that point, all lambda terms can be between 0 and 5

3. RECONSTRUCTION IS ALWATS ON. If in range do not change lambda. If out_of_range change lambda up or down. Lambda is clamped to [0.1, 10]

4. SPARSITY IS ALWAYS ON: If in range do nothing. If out_of_range change lambda up or down. Lambda is clamped to [-10, 10]. The negative part is to get out of the empty solution if necessary.

5. User should provide a fg_mask which can be easily obtained by Otsu or other thresholding methods

Overlap immediately pushes the fg_fraction to zero. That makes sense since at the beginning y_k < 0.5 and y_k(1-y_k) is minimized pushing all y_k to zero. Is there any incentives in learning non-overlapping instances (via KL) is there is no overlap?
I should reintroduce overlap as computed in terms of no-self interaction

• READ PAPERS ABOUT HOW THEY DO DYNAMICAL REGULARIZATION
• I COULD CROP THE FEATURE MAP AT THE LEVEL OF THE PGRID B/C THE POINT IS THAT THE INTERACTION IS DISCOVERED AT THE COARSER LEVEL (SIMILAR TO MASK-R_CNN)
• power of methods would come from:
  --> combining dots (like Baysor)
  --> graph consensus
• BACKGROUND LATENT CODE CAN BE 5x5. That way I can probably describe the spreading spreading I see in MERFISH

If reconstruction is in range do nothing. when parameters are in range I should not change them, i.e. change g = min(x-x_min, x_max -x) to g = min(x-x_min, x_max -x).clamp(max=0)
4. sparsity should always be on.

OLD:
3. in reconstruction is high it overcomes the sparsity term and the overlap term -> therefore lambda_rec need to multiply everything)