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Found 22 papers, 14 papers with code
Locating and Editing Factual Knowledge in GPT
We investigate the mechanisms underlying factual knowledge recall in autoregressive transformer language models.
Natural Language Descriptions of Deep Visual Features
Given a neuron, MILAN generates a description by searching for a natural language string that maximizes pointwise mutual information with the image regions in which the neuron is active.
Editing a classifier by rewriting its prediction rules
We present a methodology for modifying the behavior of a classifier by directly rewriting its prediction rules.
Toward a Visual Concept Vocabulary for GAN Latent Space
A large body of recent work has identified transformations in the latent spaces of generative adversarial networks (GANs) that consistently and interpretably transform generated images.
Natural Language Descriptions of Deep Features
Given a neuron, MILAN generates a description by searching for a natural language string that maximizes pointwise mutual information with the image regions in which the neuron is active.
Sketch Your Own GAN
In particular, we change the weights of an original GAN model according to user sketches.
Understanding the Role of Individual Units in a Deep Neural Network
Second, we use a similar analytic method to analyze a generative adversarial network (GAN) model trained to generate scenes.
What makes fake images detectable? Understanding properties that generalize
The quality of image generation and manipulation is reaching impressive levels, making it increasingly difficult for a human to distinguish between what is real and what is fake.
Rewriting a Deep Generative Model
To address the problem, we propose a formulation in which the desired rule is changed by manipulating a layer of a deep network as a linear associative memory.
Diverse Image Generation via Self-Conditioned GANs
We introduce a simple but effective unsupervised method for generating realistic and diverse images.
Semantic Photo Manipulation with a Generative Image Prior
First, it is hard for GANs to precisely reproduce an input image.
Seeing What a GAN Cannot Generate
Differences in statistics reveal object classes that are omitted by a GAN.
Dissecting Pruned Neural Networks
Namely, we consider the effect of removing unnecessary structure on the number of hidden units that learn disentangled representations of human-recognizable concepts as identified by network dissection.
Visualizing and Understanding GANs
We present an analytic framework to visualize and understand GANs at the unit-, object-, and scene-level.
On the Units of GANs (Extended Abstract)
We quantify the causal effect of interpretable units by measuring the ability of interventions to control objects in the output.
GAN Dissection: Visualizing and Understanding Generative Adversarial Networks
Then, we quantify the causal effect of interpretable units by measuring the ability of interventions to control objects in the output.
Interpretable Basis Decomposition for Visual Explanation
Explanations of the decisions made by a deep neural network are important for human end-users to be able to understand and diagnose the trustworthiness of the system.
Revisiting the Importance of Individual Units in CNNs via Ablation
We confirm that unit attributes such as class selectivity are a poor predictor for impact on overall accuracy as found previously in recent work \cite{morcos2018importance}.
Explaining Explanations: An Overview of Interpretability of Machine Learning
There has recently been a surge of work in explanatory artificial intelligence (XAI).
Interpreting Deep Visual Representations via Network Dissection
In this work, we describe Network Dissection, a method that interprets networks by providing labels for the units of their deep visual representations.
Network Dissection: Quantifying Interpretability of Deep Visual Representations
Given any CNN model, the proposed method draws on a broad data set of visual concepts to score the semantics of hidden units at each intermediate convolutional layer.
