This paper presents a visualization technique for Convolutional Neural Networks (CNNs) to better understand their internal operation and improve their performance. The authors introduce a Deconvolutional Network (deconvnet) to project feature activations back to the input pixel space, revealing which input patterns activate specific feature maps. They also perform an ablation study to determine the contribution of different model layers to performance. The visualization technique allows them to identify model architectures that outperform previous results on the ImageNet benchmark. The authors show that their ImageNet model generalizes well to other datasets, such as Caltech-101 and Caltech-256, when the softmax classifier is retrained. The paper also explores the generalization ability of CNN features and shows that the model can be used for supervised pre-training, contrasting with unsupervised pre-training methods. The authors analyze the invariance of features across different layers and show that higher layers exhibit greater invariance. They also perform occlusion experiments to determine if the model is sensitive to local image structures and not just using broad scene context. The results show that the model is highly sensitive to local structure and that the depth of the network is crucial for performance. The authors also explore the discriminative power of features in each layer of their ImageNet-pretrained model. They find that deeper layers produce more discriminative features. The paper concludes that CNNs are capable of learning complex, hierarchical features that are useful for image classification tasks. The visualization techniques presented in this paper provide valuable insights into the operation of CNNs and can be used to improve model performance. The results show that the ImageNet-trained model generalizes well to other datasets, challenging the utility of benchmarks with small training sets. However, the model generalizes less well to the PASCAL dataset, possibly due to dataset bias. The authors suggest that using a different loss function that allows for multiple objects per image could improve performance on object detection tasks.This paper presents a visualization technique for Convolutional Neural Networks (CNNs) to better understand their internal operation and improve their performance. The authors introduce a Deconvolutional Network (deconvnet) to project feature activations back to the input pixel space, revealing which input patterns activate specific feature maps. They also perform an ablation study to determine the contribution of different model layers to performance. The visualization technique allows them to identify model architectures that outperform previous results on the ImageNet benchmark. The authors show that their ImageNet model generalizes well to other datasets, such as Caltech-101 and Caltech-256, when the softmax classifier is retrained. The paper also explores the generalization ability of CNN features and shows that the model can be used for supervised pre-training, contrasting with unsupervised pre-training methods. The authors analyze the invariance of features across different layers and show that higher layers exhibit greater invariance. They also perform occlusion experiments to determine if the model is sensitive to local image structures and not just using broad scene context. The results show that the model is highly sensitive to local structure and that the depth of the network is crucial for performance. The authors also explore the discriminative power of features in each layer of their ImageNet-pretrained model. They find that deeper layers produce more discriminative features. The paper concludes that CNNs are capable of learning complex, hierarchical features that are useful for image classification tasks. The visualization techniques presented in this paper provide valuable insights into the operation of CNNs and can be used to improve model performance. The results show that the ImageNet-trained model generalizes well to other datasets, challenging the utility of benchmarks with small training sets. However, the model generalizes less well to the PASCAL dataset, possibly due to dataset bias. The authors suggest that using a different loss function that allows for multiple objects per image could improve performance on object detection tasks.