Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Deep Combinatorial Aggregation
Authors: Yuesong Shen, Daniel Cremers
NeurIPS 2022 | Venue PDF | LLM Run Details | Input Tokens: 16,971 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,412 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We experiment on in-domain, distributional shift, and out-of-distribution image classification tasks, and empirically confirm the effectiveness of DCWA and DCA approaches. |
| Researcher Affiliation | Academia | Yuesong Shen 1,2 Daniel Cremers 1,2 1 Technical University of Munich, Germany 2 Munich Center for Machine Learning, Germany EMAIL |
| Pseudocode | Yes | In Appendix A we provide a pseudo-code for layerwise DCA training. |
| Open Source Code | Yes | Source code is available at https://github.com/tum-vision/dca. |
| Open Datasets | Yes | For this we use CIFAR-10 [22] and SVHN [31] datasets |
| Dataset Splits | No | The paper uses standard datasets like CIFAR-10 and SVHN but does not explicitly provide the specific percentages or counts for training, validation, and test splits. |
| Hardware Specification | Yes | Our implementation uses Py Torch [32] and can run on a single modern GPU with 10Gb VRAM. |
| Software Dependencies | No | Our implementation uses Py Torch [32] - while PyTorch is mentioned, a specific version number is not provided, which is required for reproducible software dependencies. |
| Experiment Setup | Yes | For all training we use SGD with momentum 0.9. We use a drop rate of 0.1 for MC dropout, and for SWAG we follow the settings from Maddox et al. [28]. Deep ensemble is performed on five separate base networks. And trunkwise and modelwise DCA models also use five copies. We schedule 200 epochs for standard baseline training. Then, for training a DCA model with three sets of component instances we use 600 epochs, and accumulate for each minibatch an average gradient over three backpropagations on randomly selected DCA proposals for parameter update. |