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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Efficient Compact Bilinear Pooling via Kronecker Product
Authors: Tan Yu, Yunfeng Cai, Ping Li3170-3178
AAAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Systematic experiments on four public benchmarks using two backbones demonstrate the efficiency and effectiveness of the proposed method in fine-grained recognition. |
| Researcher Affiliation | Industry | Tan Yu, Yunfeng Cai, Ping Li Cognitive Computing Lab Baidu Research 10900 NE 8th St. Bellevue, Washington 98004, USA No.10 Xibeiwang East Road, Beijing 100193, China EMAIL |
| Pseudocode | Yes | Algorithm 1: Tensor Modal Product 1: Input: r, X Rd N, b A R a r d r . 2: Output: T = [Ir b A]X. 3: Reshape X into a tensor X RN d r r. 4: Perform modal product T = X 2 b A 3 Ir. 5: Unfold the tensor T along mode-1, and set T = T (1). |
| Open Source Code | No | The paper does not contain any explicit statement about releasing source code or a link to a code repository for the described methodology. |
| Open Datasets | Yes | We conduct experiments on four public benchmarks for fine-grained recognition including FGVC-Aircraft (AIR) (Maji et al. 2013), CUB-200-2011 (CUB) (Wah et al. 2011), MIT scene dataset (Quattoni and Torralba 2009), and Describable Texture Dataset (DTD) (Cimpoi et al. 2014). |
| Dataset Splits | No | The paper mentions using 'public benchmarks' but does not explicitly specify the training/validation/test dataset splits (e.g., percentages or sample counts) within the text. |
| Hardware Specification | Yes | The experiments are conducted on a single NVIDIA Titan X (Pascal) GPU card. |
| Software Dependencies | No | The paper states that the method is 'implemented in Paddle Paddle platform' but does not provide specific version numbers for PaddlePaddle or any other software dependencies. |
| Experiment Setup | Yes | We adopt a two-phase training. In the first phase, we only update parameters in TKPF and classifier layers. In the second phase, we fine-tune parameters of all layers. Each image is resized into 448 448... By default, we set a = b = 96, that is, D = 962. We set r = 32 by default when using VGG16 backbone. Considering both effectiveness and efficiency, we set Q = 2 by default. |