SlowGuess/NeurIPS_2023_OCR huggingface dataset:
Algorithm 1 Intervention selector Pseudocode
$X\gets$  input image   
 $c_{1}\dots c_{n}\gets n$  intermediary concepts   
 $Y\gets$  label   
 $g\gets$  Image to concept prediction model   
 $f\gets$  Concept to label prediction model   
 $c_{1}\dots c_{n} = g(X)$    
for  $i = 1,2,\ldots ,n$  do  $\mathcal{H}_i = CUS(c_i)$ $\beta_{i} = CWS(c_{i})$ $\gamma_{i} = SCS(c_{i})$ $\hat{c}_i = k_1*\mathcal{H}_i + k_2*\beta_i + k_3*\gamma_i$    
end for   
 $\hat{c}_{1}\dots \hat{c}_{thr}\dots \hat{c}_{n}\gets$  threshold   
 $\bar{c}_{1}\dots \bar{c}_{thr}\gets$  intervene on threshold valued   
 $Y^{\prime} = f(\bar{c}_{1}\dots \bar{c}_{n})$
F Example of OOD data
Figure 6: L: example from trainset, M: example from in-domain testset, R: example from out-domain testset from spurious background correlation CUB synthetic dataset, class - Black Footed Albatross
2This similarity metric is pre-defined by humans
Figure 4: Interventions on Joint CBM in the presence of image corruptions on CUB dataset
Figure 5: Interventions on Joint CBM in the presence of image corruptions on CUB dataset
G Limitations and Future Work
In this work, we proposed a novel concept-based approach, coop-CBM, to enhance the interpretability and accuracy trade-off in AI models. We introduced the Concept Orthogonal Loss (COL) to improve concept learning and employed Coop-CBM on various datasets and evaluation scenarios. Our results demonstrated superior generalization, robustness to spurious correlations, and advancements in the accuracy-interpretability trade-off.
While our proposed approach has shown promising results and made significant contributions, it is important to recognize certain limitations. Firstly, the reliance on labeled concept vectors poses challenges in domains where concept annotations are limited or costly to acquire. Furthermore, current concept annotation methods suffer from biases and a lack of domain knowledge, indicating the need for further improvements in this area. Our work does not address unsupervised concept acquisition methods, but instead focuses on a model architecture that can be applied regardless of the concept acquisition approach.
While our concept-based approach offers interpretability, it assumes that the learned concepts align closely with human-understandable notions. However, there is a possibility that the learned concepts may not always perfectly align with the intended interpretations, which can pose challenges in terms of comprehensibility and explainability. Future research could delve deeper into understanding explanations and their alignment with human understanding, thereby exploring ways to improve the fidelity of concept-based models in providing accurate and meaningful explanations. Future research could aim to incorporate additional evaluation metrics that assess the transparency, fairness, and robustness aspects of concept-based models.
Overall, while our approach shows promising results and addresses important concerns in the field of explainable AI, it is important to be aware of these limitations and continue advancing research to overcome them and further enhance the applicability and reliability of concept-based models.