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..
Toward a neuro-inspired creative decoder
Authors: Payel Das, Brian Quanz, Pin-Yu Chen, Jae-wook Ahn, Dhruv Shah
IJCAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments on different image datasets (MNIST, FMNIST, MNIST+FMNIST, Wiki Art and Celeb A) reveal that atypical co-activation of highly activated and weakly activated neurons in a deep decoder promotes generation of novel and meaningful artifacts. |
| Researcher Affiliation | Industry | Payel Das , Brian Quanz , Pin-Yu Chen , Jae-wook Ahn and Dhruv Shah IBM Research, Yorktown Heights, NY, USA EMAIL, EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 Low-active method |
| Open Source Code | No | The paper does not provide an explicit statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | We show the performance of the proposed method against MNIST digits , FMINST fashion objects , and on a combined MNIST plus FMINST dataset. We also present results on the Wiki Art art images and Celeb A faces. |
| Dataset Splits | No | The paper uses standard datasets like MNIST and FMNIST and mentions training and testing but does not explicitly state the specific dataset splits (e.g., percentages or counts for train/validation/test sets) used for reproducibility. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments, such as GPU/CPU models or memory specifications. |
| Software Dependencies | No | The paper mentions deep learning frameworks like VAE and GAN but does not specify software dependencies with version numbers (e.g., Python, PyTorch, or TensorFlow versions). |
| Experiment Setup | Yes | For F/MNIST the encoder network consisted of 3 fully-connected layers (1000, 500, 250) before the z output (50 for F/MNIST and 100 for the combination), with the decoder architecture the reverse of the encoder. RELU activations were used; dropout equal to 0.10 for fully-connected layers was used during training only. ... Unless otherwise stated, results in the main paper were obtained by perturbing five neurons during decoding. For the low-active method, we used neurons whose activations (see Method Section) were within the 1st and 15th percentiles of the neuron percent activations (ak j ) for the layer. |