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Masksembles for Uncertainty Estimation

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Why Masksembles?

Uncertainty Estimation is one of the most important and critical tasks in the area of modern neural networks and deep learning. There is a long list of potential applications of uncertainty: safety-critical applications, active learning, domain adaptation, reinforcement learning and etc.

Masksembles is a simple and easy-to-use drop-in method with performance on par with Deep Ensembles at a fraction of the cost. It makes almost no changes in your original model and requires only to add special intermediate layers.

Installation

To install this package, use:

pip install git+http://github.com/nikitadurasov/masksembles

In addition, Masksembles requires installing at least one of the backends: torch or tensorflow2 / keras. Please follow official installation instructions for torch or tensorflow accordingly.

Usage

This package provides implementations for Masksembles{1|2|3}D layers in masksembles.{torch|keras} where {1|2|3} refers to dimensionality of input tensors (1-, 2- and 3-dimensional accordingly).

• Masksembles1D: works with 1-dim inputs,[B, C] shaped tensors
• Masksembles2D: works with 2-dim inputs,[B, H, W, C] (keras) or [B, C, H, W] (torch) shaped tensors
• Masksembles3D : TBD

In a Nutshell, Masksembles applies binary masks to inputs via multiplying them both channel-wise. For more efficient implementation we've followed approach similar to this one. Therefore, after inference outputs[:B // N] - stores results for the first submodel, outputs[B // N : 2 * B // N] - for the second and etc.

Torch

import torch
from masksembles.torch import Masksembles1D

layer = Masksembles1D(10, 4, 2.)
layer(torch.ones([4, 10]))

tensor([[0., 1., 0., 0., 1., 0., 1., 1., 1., 1.],
        [0., 0., 1., 1., 1., 1., 0., 0., 1., 1.],
        [1., 0., 1., 1., 0., 0., 1., 0., 1., 1.],
        [1., 0., 0., 1., 1., 1., 0., 1., 1., 0.]], dtype=torch.float64)

Tensorflow / Keras

import tensorflow as tf 
from masksembles.keras import Masksembles1D

layer = Masksembles1D(4, 2.)
layer(tf.ones([4, 10]))

<tf.Tensor: shape=(4, 10), dtype=float32, numpy=
array([[0., 1., 1., 0., 1., 1., 1., 0., 1., 0.],
       [0., 1., 0., 1., 1., 0., 1., 1., 0., 1.],
       [1., 1., 1., 1., 0., 0., 1., 0., 0., 1.],
       [1., 0., 0., 1., 0., 1., 1., 0., 1., 1.]], dtype=float32)>

Model example

import tensorflow as tf 
from masksembles.keras import Masksembles1D, Masksembles2D

model = keras.Sequential(
    [
        keras.Input(shape=input_shape),
        layers.Conv2D(32, kernel_size=(3, 3), activation="elu"),
        Masksembles2D(4, 2.0),
        layers.MaxPooling2D(pool_size=(2, 2)),
     
        layers.Conv2D(64, kernel_size=(3, 3), activation="elu"),
        Masksembles2D(4, 2.0),
        layers.MaxPooling2D(pool_size=(2, 2)),
     
        layers.Flatten(),
        Masksembles1D(4, 2.),
        layers.Dense(num_classes, activation="softmax"),
    ]
)

Citation

If you found this work useful for your projects, please don't forget to cite it.

@inproceedings{Durasov21,
  author = {N. Durasov and T. Bagautdinov and P. Baque and P. Fua},
  title = {{Masksembles for Uncertainty Estimation}},
  booktitle = CVPR,
  year = 2021
}