Negative Results in Computer Vision: A Perspective

A negative result is when the outcome of an experiment or a model is not what is expected or when a hypothesis does not hold. Despite being often overlooked in the scientific community, negative results are results and they carry value. While this topic has been extensively discussed in other fields such as social sciences and biosciences, less attention has been paid to it in the computer vision community. The unique characteristics of computer vision, particularly its experimental aspect, call for a special treatment of this matter. In this paper, I will address what makes negative results important, how they should be disseminated and incentivized, and what lessons can be learned from cognitive vision research in this regard. Further, I will discuss issues such as computer vision and human vision interaction, experimental design and statistical hypothesis testing, explanatory versus predictive modeling, performance evaluation, model comparison, as well as computer vision research culture

And/or trade-off in artificial neurons: impact on adversarial robustness

Since its discovery in 2013, the phenomenon of adversarial examples has attracted a growing amount of attention from the machine learning community. A deeper understanding of the problem could lead to a better comprehension of how information is processed and encoded in neural networks and, more in general, could help to solve the issue of interpretability in machine learning. Our idea to increase adversarial resilience starts with the observation that artificial neurons can be divided in two broad categories: AND-like neurons and OR-like neurons. Intuitively, the former are characterised by a relatively low number of combinations of input values which trigger neuron activation, while for the latter the opposite is true. Our hypothesis is that the presence in a network of a sufficiently high number of OR-like neurons could lead to classification "brittleness" and increase the network's susceptibility to adversarial attacks. After constructing an operational definition of a neuron AND-like behaviour, we proceed to introduce several measures to increase the proportion of AND-like neurons in the network: L1 norm weight normalisation; application of an input filter; comparison between the neuron output's distribution obtained when the network is fed with the actual data set and the distribution obtained when the network is fed with a randomised version of the former called "scrambled data set". Tests performed on the MNIST data set hint that the proposed measures could represent an interesting direction to explore

Disentangling Adversarial Robustness and Generalization

Obtaining deep networks that are robust against adversarial examples and generalize well is an open problem. A recent hypothesis even states that both robust and accurate models are impossible, i.e., adversarial robustness and generalization are conflicting goals. In an effort to clarify the relationship between robustness and generalization, we assume an underlying, low-dimensional data manifold and show that: 1. regular adversarial examples leave the manifold; 2. adversarial examples constrained to the manifold, i.e., on-manifold adversarial examples, exist; 3. on-manifold adversarial examples are generalization errors, and on-manifold adversarial training boosts generalization; 4. regular robustness and generalization are not necessarily contradicting goals. These assumptions imply that both robust and accurate models are possible. However, different models (architectures, training strategies etc.) can exhibit different robustness and generalization characteristics. To confirm our claims, we present extensive experiments on synthetic data (with known manifold) as well as on EMNIST, Fashion-MNIST and CelebA.Comment: Conference on Computer Vision and Pattern Recognition 201