This paper introduces a novel visualization technique to understand the internal operations of large Convolutional Neural Networks (CNNs) and improve their performance. The authors use a multi-layered Deconvolutional Network (deconvnet) to project feature activations back to the input pixel space, revealing the input stimuli that excite individual feature maps. This visualization technique helps in diagnosing model issues and selecting better architectures. The authors demonstrate that their approach can lead to improved performance on the ImageNet classification benchmark compared to the state-of-the-art model by Krizhevsky et al. They also show that the model generalizes well to other datasets, such as Caltech-101 and Caltech-256, achieving better results than current state-of-the-art methods. The paper includes detailed experiments and ablation studies to validate the effectiveness of the proposed visualization and architectural improvements.This paper introduces a novel visualization technique to understand the internal operations of large Convolutional Neural Networks (CNNs) and improve their performance. The authors use a multi-layered Deconvolutional Network (deconvnet) to project feature activations back to the input pixel space, revealing the input stimuli that excite individual feature maps. This visualization technique helps in diagnosing model issues and selecting better architectures. The authors demonstrate that their approach can lead to improved performance on the ImageNet classification benchmark compared to the state-of-the-art model by Krizhevsky et al. They also show that the model generalizes well to other datasets, such as Caltech-101 and Caltech-256, achieving better results than current state-of-the-art methods. The paper includes detailed experiments and ablation studies to validate the effectiveness of the proposed visualization and architectural improvements.