@article{14739,
  abstract     = {Attempts to incorporate topological information in supervised learning tasks have resulted in the creation of several techniques for vectorizing persistent homology barcodes. In this paper, we study thirteen such methods. Besides describing an organizational framework for these methods, we comprehensively benchmark them against three well-known classification tasks. Surprisingly, we discover that the best-performing method is a simple vectorization, which consists only of a few elementary summary statistics. Finally, we provide a convenient web application which has been designed to facilitate exploration and experimentation with various vectorization methods.},
  author       = {Ali, Dashti and Asaad, Aras and Jimenez, Maria-Jose and Nanda, Vidit and Paluzo-Hidalgo, Eduardo and Soriano Trigueros, Manuel},
  issn         = {1939-3539},
  journal      = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
  keywords     = {Applied Mathematics, Artificial Intelligence, Computational Theory and Mathematics, Computer Vision and Pattern Recognition, Software},
  number       = {12},
  pages        = {14069--14080},
  publisher    = {IEEE},
  title        = {{A survey of vectorization methods in topological data analysis}},
  doi          = {10.1109/tpami.2023.3308391},
  volume       = {45},
  year         = {2023},
}

@article{6554,
  abstract     = {Due to the importance of zero-shot learning, i.e. classifying images where there is a lack of labeled training data, the number of proposed approaches has recently increased steadily. We argue that it is time to take a step back and to analyze the status quo of the area. The purpose of this paper is three-fold. First, given the fact that there is no agreed upon zero-shot learning benchmark, we first define a new benchmark by unifying both the evaluation protocols and data splits of publicly available datasets used for this task. This is an important contribution as published results are often not comparable and sometimes even flawed due to, e.g. pre-training on zero-shot test classes. Moreover, we propose a new zero-shot learning dataset, the Animals with Attributes 2 (AWA2) dataset which we make publicly available both in terms of image features and the images themselves. Second, we compare and analyze a significant number of the state-of-the-art methods in depth, both in the classic zero-shot setting but also in the more realistic generalized zero-shot setting. Finally, we discuss in detail the limitations of the current status of the area which can be taken as a basis for advancing it.},
  author       = {Xian, Yongqin and Lampert, Christoph and Schiele, Bernt and Akata, Zeynep},
  issn         = {1939-3539},
  journal      = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
  number       = {9},
  pages        = {2251 -- 2265},
  publisher    = {Institute of Electrical and Electronics Engineers (IEEE)},
  title        = {{Zero-shot learning - A comprehensive evaluation of the good, the bad and the ugly}},
  doi          = {10.1109/tpami.2018.2857768},
  volume       = {41},
  year         = {2019},
}