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Found 8 papers, 4 papers with code
Automatic Discovery of Visual Circuits
To date, most discoveries of network subcomponents that implement human-interpretable computations in deep vision models have involved close study of single units and large amounts of human labor.
A Multimodal Automated Interpretability Agent
Interpretability experiments proposed by MAIA compose these tools to describe and explain system behavior.
FIND: A Function Description Benchmark for Evaluating Interpretability Methods
FIND contains functions that resemble components of trained neural networks, and accompanying descriptions of the kind we seek to generate.
Multimodal Neurons in Pretrained Text-Only Transformers
Language models demonstrate remarkable capacity to generalize representations learned in one modality to downstream tasks in other modalities.
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.
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.
Latent Compass: Creation by Navigation
Our approach puts creators in the discovery loop during real-time tool use, in order to identify directions that are perceptually meaningful to them, and generate interpretable image translations along those directions.
