We lifted 7 functions out of this paper's own repositories and ran 6 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| sacadena/diverse_feature_vis | canonical | 6 of 7 |
| Function | Status | Where it lives |
|---|---|---|
| concat_elu | Ran | sacadena/diverse_feature_vis/pixel-cnn/pixel_cnn_pp/nn.py code served (permissive licence) · get_code("919f423798c6a32a") |
| conv_filter_tile | Ran | sacadena/diverse_feature_vis/pixel-cnn/utils/plotting.py code served (permissive licence) · get_code("0cb6bf5e8d04ced1") |
| deprocess_image | Ran | sacadena/diverse_feature_vis/diverse_vis_functions.py code served (permissive licence) · get_code("e5e8e8a0eb923ad9") |
| img_stretch | Ran | sacadena/diverse_feature_vis/pixel-cnn/utils/plotting.py code served (permissive licence) · get_code("6b93fbdaf0e2b9e2") |
| img_tile | Ran | sacadena/diverse_feature_vis/pixel-cnn/utils/plotting.py code served (permissive licence) · get_code("6c57db0a5a8111ab") |
| int_shape | Ran | sacadena/diverse_feature_vis/pixel-cnn/pixel_cnn_pp/nn.py code served (permissive licence) · get_code("3ba6aec873833b39") |
| log_sum_exp | Not yet run | sacadena/diverse_feature_vis/pixel-cnn/pixel_cnn_pp/nn.py code served (permissive licence) · get_code("ba937f5383eb9016") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Visualizing features in deep neural networks (DNNs) can help understanding their computations. Many previous studies aimed to visualize the selectivity of individual units by finding meaningful images that maximize their activation. However, comparably little attention has been paid to visualizing to what image transformations units in DNNs are invariant. Here we propose a method to discover invariances in the responses of hidden layer units of deep neural networks. Our approach is based on simultaneously searching for a batch of images that strongly activate a unit while at the same time being as distinct from each other as possible. We find that even early convolutional layers in VGG-19 exhibit various forms of response invariance: near-perfect phase invariance in some units and invariance to local diffeomorphic transformations in others. At the same time, we uncover representational differences with ResNet-50 in its corresponding layers. We conclude that invariance transformations are a major computational component learned by DNNs and we provide a systematic method to study them.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("1807.10589")
get_code_for_paper("1807.10589")
have("1807.10589")
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