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Neural Concept Binder

Wolfgang Stammer, Antonia Wüst, David Steinmann, Kristian Kersting

TL;DR

The Neural Concept Binder is introduced, a novel framework for deriving both discrete and continuous concept representations, which are referred to as concept-slot encodings, and it is demonstrated that incorporating the hard binding mechanism preserves model performance while enabling seamless integration into both neural and symbolic modules for complex reasoning tasks.

Abstract

The challenge in object-based visual reasoning lies in generating concept representations that are both descriptive and distinct. Achieving this in an unsupervised manner requires human users to understand the model's learned concepts and, if necessary, revise incorrect ones. To address this challenge, we introduce the Neural Concept Binder (NCB), a novel framework for deriving both discrete and continuous concept representations, which we refer to as "concept-slot encodings". NCB employs two types of binding: "soft binding", which leverages the recent SysBinder mechanism to obtain object-factor encodings, and subsequent "hard binding", achieved through hierarchical clustering and retrieval-based inference. This enables obtaining expressive, discrete representations from unlabeled images. Moreover, the structured nature of NCB's concept representations allows for intuitive inspection and the straightforward integration of external knowledge, such as human input or insights from other AI models like GPT-4. Additionally, we demonstrate that incorporating the hard binding mechanism preserves model performance while enabling seamless integration into both neural and symbolic modules for complex reasoning tasks. We validate the effectiveness of NCB through evaluations on our newly introduced CLEVR-Sudoku dataset.

Neural Concept Binder

TL;DR

The Neural Concept Binder is introduced, a novel framework for deriving both discrete and continuous concept representations, which are referred to as concept-slot encodings, and it is demonstrated that incorporating the hard binding mechanism preserves model performance while enabling seamless integration into both neural and symbolic modules for complex reasoning tasks.

Abstract

The challenge in object-based visual reasoning lies in generating concept representations that are both descriptive and distinct. Achieving this in an unsupervised manner requires human users to understand the model's learned concepts and, if necessary, revise incorrect ones. To address this challenge, we introduce the Neural Concept Binder (NCB), a novel framework for deriving both discrete and continuous concept representations, which we refer to as "concept-slot encodings". NCB employs two types of binding: "soft binding", which leverages the recent SysBinder mechanism to obtain object-factor encodings, and subsequent "hard binding", achieved through hierarchical clustering and retrieval-based inference. This enables obtaining expressive, discrete representations from unlabeled images. Moreover, the structured nature of NCB's concept representations allows for intuitive inspection and the straightforward integration of external knowledge, such as human input or insights from other AI models like GPT-4. Additionally, we demonstrate that incorporating the hard binding mechanism preserves model performance while enabling seamless integration into both neural and symbolic modules for complex reasoning tasks. We validate the effectiveness of NCB through evaluations on our newly introduced CLEVR-Sudoku dataset.
Paper Structure (34 sections, 1 equation, 17 figures, 8 tables, 1 algorithm)

This paper contains 34 sections, 1 equation, 17 figures, 8 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. Neural Concept Binder (NCB): Extracting Hard from Soft Concepts
  4. Inferring Concept-Slot Representations
  5. Unsupervised Concept Learning via NCB
  6. Inspecting and Revising NCB's Concepts
  7. Experimental Evaluations
  8. Evaluations
  9. Conclusions
  10. Details on Neural Concept Binder
  11. Details on Systematic Binding and Slot Attention
  12. Selection Function
  13. Details on Training
  14. Instantiating Neural Concept Binder
  15. Computational Resources
  16. ...and 19 more sections

Figures (17)

  • Figure 1: Unsupervised learning of concepts for visual reasoning. (left) Models that learn concepts from unlabeled data require inspectable and revisable concept representations. (right) Concepts obtained from the Neural Concept Binder (NCB) can be utilized both in (interpretable) neural and symbolic computations.
  • Figure 2: The Neural Concept Binder (NCB) combines continuous, block-slot encodings via slot-attention based image processing with discrete, concept-slot encodings via retrieval-based inference. The structured retrieval corpus (distilled from the block-slot encodings) allows for easy concept inspection and revision by human stakeholders. Moreover, the resulting concept-slot encodings can be easily integrated into complex downstream tasks.
  • Figure 3: NCB's concept space is inherently inspectable. A human stakeholder can easily inspect the concept space by asking a diverse set of questions. For example, NCB answers interventional questions (iii) via generating images with selectively modified concepts.
  • Figure 4: Example from CLEVR-Sudoku. Each digit is represented by CLEVR objects with the same attribute combination. The objective is to solve the Sudoku only based on the initial grid of CLEVR images and the digit mapping of candidate examples.
  • Figure 5: NCB's unsupervised concepts allow solving symbolic puzzles. Accuracy of solved Sudokus via different discrete concept encodings on Sudoku CLEVR-Easy and Sudoku CLEVR (left sides). Additional revision on NCB's concepts leads to improved performances (right sides).
  • ...and 12 more figures