We lifted 20 functions out of this paper's own repositories and ran 9 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 |
|---|---|---|
| liujiaheng/iclr_17_compression | pwc_unofficial | 5 of 12 |
| copy not recorded | — | 4 of 4 |
| treammm/Compression | pwc_unofficial | 0 of 4 |
| Function | Status | Where it lives |
|---|---|---|
| Average | Ran | this paper's copy was not recorded; identical code first harvested from InterDigitalInc/CompressAI pointer only · get_code("390aec91fdddfcac") |
| BoolConvert | Ran | this paper's copy was not recorded; identical code first harvested from InterDigitalInc/CompressAI pointer only · get_code("d729e65bfe314ae3") |
| CalcuPSNR | Ran | liujiaheng/iclr_17_compression/models/basics.py code served (permissive licence) · get_code("c9947c33e3a54f62") |
| get_loader | Ran | liujiaheng/iclr_17_compression/datasets.py code served (permissive licence) · get_code("330dd4378c3e3a35") |
| get_train_loader | Ran | liujiaheng/iclr_17_compression/datasets.py code served (permissive licence) · get_code("4e3ec336e202299e") |
| inverse_dict | Ran | this paper's copy was not recorded; identical code first harvested from InterDigitalInc/CompressAI pointer only · get_code("5dff248eb7143d81") |
| print_dict | Ran | this paper's copy was not recorded; identical code first harvested from gergely-flamich/relative-entropy-coding pointer only · get_code("73b28d07dc4d8995") |
| relu | Ran | liujiaheng/iclr_17_compression/models/basics.py code served (permissive licence) · get_code("ba24f201a717509b") |
| ssim | Ran | liujiaheng/iclr_17_compression/models/ms_ssim_torch.py code served (permissive licence) · get_code("d9e269e86cc2b394") |
| MultiScaleSSIM | Not yet run | liujiaheng/iclr_17_compression/metric.py code served (permissive licence) · get_code("a5a05f863cc1b904") |
| gaussian_filter | Not yet run | liujiaheng/iclr_17_compression/models/ms_ssim_torch.py code served (permissive licence) · get_code("569f86999a28550f") |
| irdft_matrix | Not yet run | treammm/Compression/tensorflow_compression/python/ops/spectral_ops.py code served (permissive licence) · get_code("e1be96227f48724d") |
| load_model | Not yet run | liujiaheng/iclr_17_compression/model.py code served (permissive licence) · get_code("67b83d1f47e01c3f") |
| lower_bound | Not yet run | treammm/Compression/tensorflow_compression/python/ops/math_ops.py code served (permissive licence) · get_code("a863fa9d143a5fb3") |
| ms_ssim | Not yet run | liujiaheng/iclr_17_compression/models/ms_ssim_torch.py code served (permissive licence) · get_code("7ea107a5705e6f95") |
| msssim | Not yet run | liujiaheng/iclr_17_compression/metric.py code served (permissive licence) · get_code("c46a2076f53009e8") |
| psnr | Not yet run | liujiaheng/iclr_17_compression/metric.py code served (permissive licence) · get_code("94c8354fe1b4c307") |
| same_padding_for_kernel | Not yet run | treammm/Compression/tensorflow_compression/python/ops/padding_ops.py code served (permissive licence) · get_code("680ff131d22fbc0b") |
| upper_bound | Not yet run | treammm/Compression/tensorflow_compression/python/ops/math_ops.py code served (permissive licence) · get_code("fc9f7ea55d17f349") |
| yuv_import_444 | Not yet run | liujiaheng/iclr_17_compression/models/basics.py code served (permissive licence) · get_code("2784ebaef09c1269") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
We describe an image compression method, consisting of a nonlinear analysis transformation, a uniform quantizer, and a nonlinear synthesis transformation. The transforms are constructed in three successive stages of convolutional linear filters and nonlinear activation functions. Unlike most convolutional neural networks, the joint nonlinearity is chosen to implement a form of local gain control, inspired by those used to model biological neurons. Using a variant of stochastic gradient descent, we jointly optimize the entire model for rate-distortion performance over a database of training images, introducing a continuous proxy for the discontinuous loss function arising from the quantizer. Under certain conditions, the relaxed loss function may be interpreted as the log likelihood of a generative model, as implemented by a variational autoencoder. Unlike these models, however, the compression model must operate at any given point along the rate-distortion curve, as specified by a trade-off parameter. Across an independent set of test images, we find that the optimized method generally exhibits better rate-distortion performance than the standard JPEG and JPEG 2000 compression methods. More importantly, we observe a dramatic improvement in visual quality for all images at all bit rates, which is supported by objective quality estimates using MS-SSIM.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("1611.01704")
get_code_for_paper("1611.01704")
have("1611.01704")
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