219 citations · 224 across the 3 of their papers we have counts for
6 papers · 1 filter
Contextual Semantic Interpretability
Diego Marcos, Ruth Fong, Sylvain Lobry +3
Convolutional neural networks (CNN) are known to learn an image representation that captures concepts relevant to the task, but do so in an implicit way that hampers model interpre…
There and Back Again: Revisiting Backpropagation Saliency Methods
Sylvestre-Alvise Rebuffi, Ruth Fong, Xu Ji +1
Saliency methods seek to explain the predictions of a model by producing an importance map across each input sample. A popular class of such methods is based on backpropagating a s…
NormGrad: Finding the Pixels that Matter for Training
Sylvestre-Alvise Rebuffi, Ruth Fong, Xu Ji +2
The different families of saliency methods, either based on contrastive signals, closed-form formulas mixing gradients with activations or on perturbation masks, all focus on which…
Understanding Deep Networks via Extremal Perturbations and Smooth Masks
Ruth Fong, Mandela Patrick, Andrea Vedaldi
The problem of attribution is concerned with identifying the parts of an input that are responsible for a model's output. An important family of attribution methods is based on mea…
Net2Vec: Quantifying and Explaining how Concepts are Encoded by Filters in Deep Neural Networks
Ruth Fong, Andrea Vedaldi
In an effort to understand the meaning of the intermediate representations captured by deep networks, recent papers have tried to associate specific semantic concepts to individual…
Using Human Brain Activity to Guide Machine Learning
Ruth Fong, Walter Scheirer, David Cox
Machine learning is a field of computer science that builds algorithms that learn. In many cases, machine learning algorithms are used to recreate a human ability like adding a cap…