masksembles 1.1.1

Official implementation of Masksembles approach

Masksembles for Uncertainty Estimation

Project Page | Paper | Video Explanation

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.

Watch this video on YouTube

Installation

To install this package, use:

pip install masksembles

or

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
}