Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Classification-by-Components: Probabilistic Modeling of Reasoning over a Set of Components
Authors: Sascha Saralajew, Lars Holdijk, Maike Rees, Ebubekir Asan, Thomas Villmann
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The evaluation of the approach on MNIST shows that CBCs are viable classifiers. Additionally, we demonstrate that the inherent interpretability offers a profound understanding of the classification behavior such that we can explain the success of an adversarial attack. The method s scalability is successfully tested using the IMAGENET dataset. |
| Researcher Affiliation | Collaboration | 1Dr. Ing. h.c. F. Porsche AG, Weissach, Germany, EMAIL 2University of Applied Sciences Mittweida, Mittweida, Germany, EMAIL |
| Pseudocode | No | The paper describes the network architecture and training process conceptually, but does not include any formal pseudocode or algorithm blocks. |
| Open Source Code | Yes | The source code is available at www.github.com/saralajew/cbc_networks. |
| Open Datasets | Yes | We evaluate CBCs on MNIST [40] and IMAGENET [41]. |
| Dataset Splits | Yes | The final test accuracies of both models are quasi equivalent and on average over three runs (97.33 0.19) %. The top-5 validation accuracy of 82.4% is on par with earlier CNN generations such as Alex Net with 82.8% [48]. |
| Hardware Specification | Yes | The CBC was evaluated using one NVIDIA Tesla V100 32 GB GPU. |
| Software Dependencies | No | The paper mentions using specific optimizers (Adam) and activation functions (Swish, ReLU) and architectures (ResNet-50) by name and citation, but it does not specify software versions for any libraries or frameworks (e.g., PyTorch, TensorFlow, Python version). |
| Experiment Setup | Yes | The CBCs use the cosine similarity with Re LU activation as detection probability function. They are trained with the margin loss defined as Eq. (2) with φ (x) = Re LU(x + β), where β is a margin parameter, using the Adam optimizer [42]. The CNN feature extractors are implemented without the use of batch normalization [43], with Swish activation [44], and the convolutional filters constraint to a Euclidean norm of one. We trained the components and reasoning probabilities from scratch using random initialization. Moreover, the margin parameter β was set to 0.3. |