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..
IMF: Integrating Matched Features Using Attentive Logit in Knowledge Distillation
Authors: Jeongho Kim, Hanbeen Lee, Simon S. Woo
IJCAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through extensive experiments, we demonstrate that IMF consistently outperforms other state-of-the-art methods with a large margin over the various datasets in different tasks without extra computation. |
| Researcher Affiliation | Collaboration | Jeongho Kim1 , Hanbeen Lee2 , Simon S. Woo3 1Korea Advanced Institute of Science and Technology (KAIST), S. Korea 2NAVER Z Corporation, S. Korea 3Department of Artificial Intelligence, Sungkyunkwan University, S. Korea |
| Pseudocode | No | The paper includes mathematical equations and descriptive text for the method, but no explicit pseudocode or algorithm block. |
| Open Source Code | No | The paper does not contain an explicit statement about releasing source code or a link to a code repository. |
| Open Datasets | Yes | For example, in CIFAR100 [Krizhevsky and Hinton, 2009], which has 100 classes, 100 parameters are added to the output logits of each IFD layer. |
| Dataset Splits | Yes | Training details. Backbone architecture and training settings for experiments are similar to the recent research [Tian et al., 2019]. |
| Hardware Specification | No | The paper mentions parameters and FLOPs for model size comparison but does not specify the hardware (e.g., GPU or CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper refers to architecture components like 'Depth Conv(3x3) Point Conv(1x1) BN & Re LU' but does not list any specific software dependencies with version numbers (e.g., PyTorch 1.9, TensorFlow 2.x). |
| Experiment Setup | Yes | Training details. Backbone architecture and training settings for experiments are similar to the recent research [Tian et al., 2019]. In our method, we conduct a grid search to choose the α and β values in Eq. 5 from {10, 20, 30, 40}. The IFD block has the same structure in all experiments and model architectures. Specifically, we used a block structure of Depth Conv(3 3) Point Conv(1 1) BN & Re LU. |