reproducibilityindex.ai

CLIPAway: Harmonizing focused embeddings for removing objects via diffusion models

Authors: Yiğit Ekin, Ahmet Burak Yildirim, Erdem Eren Çağlar, Aykut Erdem, Erkut Erdem, Aysegul Dundar

Reproducibility Variable	Result	LLM Response
Research Type	Experimental	We provide comprehensive evaluations on a standard dataset, demonstrating consistent improvements over state-of-the-art methods.
Researcher Affiliation	Academia	Department of Computer Engineering, Bilkent University, Ankara, Turkey Department of Computer Engineering, Koç University, Istanbul, Turkey KUIS AI Center, Koç University, Istanbul, Turkey Department of Computer Engineering, Hacettepe University, Ankara, Turkey
Pseudocode	Yes	Algorithm 1 Algorithm for our training and inference
Open Source Code	Yes	Code and models are available via our project website: https://yigitekin.github.io/CLIPAway/.
Open Datasets	Yes	We evaluate the models on the COCO 2017 validation dataset [15], which provides us with the collection of images indoor and outdoor and instance level annotations.
Dataset Splits	Yes	We evaluate the models on the COCO 2017 validation dataset [15], which provides us with the collection of images indoor and outdoor and instance level annotations.
Hardware Specification	Yes	The MLP model is trained on a single NVIDIA A40 GPU with batch size 8.
Software Dependencies	No	No specific version numbers for software dependencies (e.g., Python, PyTorch, CUDA) are explicitly provided in the paper.
Experiment Setup	Yes	Adam, optimizer is used with learning rate and weight decay are set to 1e 5 and 1e 4, respectively. The MLP model is trained on a single NVIDIA A40 GPU with batch size 8.