When spurious correlations between targets and data are present in training samples, deep neural networks may struggle to generalize, typically learning shortcuts corresponding to such undesired correlations (i.e., the bias), rather than fundamental target attributes. If bias attributes are assumed to be known, several strategies can be applied to mitigate a model’s dependency on bias, including upsampling or upweighting samples with no bias. However, this is hardly the case in real-world scenarios, and alternative unsupervised debiasing approaches have been proposed in recent years, assuming no bias information is available. In this work, we propose Lose Your Self (LoYS), a novel bias-unsupervised approach for model debiasing. The main design strategy in LoYS aims to force a model learning semantic features, discouraging it from learning bias-related descriptors, specifically focusing on the target classifier confidence. Exploiting an adversarial scheme based on entropy loss, against a shallow auxiliary classifier trained to match the predictions of a pre-trained biased model, and entropy regularizations, our experiments show how LoYS is competitive or outperforms state-of-the-art methods, on several common benchmarks.

The effectiveness of deep learning models in classification tasks is often challenged by the quality and quantity of training data whenever they are affected by strong spurious correlations between specific attributes and target labels. This results in a form of bias affecting training data, which typically leads to unrecoverable weak generalization in prediction. This paper addresses this problem by leveraging bias amplification with generated synthetic data only: we introduce Diffusing DeBias (DDB), a novel approach acting as a plug-in for common methods of unsupervised model debiasing, exploiting the inherent bias-learning tendency of diffusion models in data generation. Specifically, our approach adopts conditional diffusion models to generate synthetic bias-aligned images, which fully replace the original training set for learning an effective bias amplifier model to be subsequently incorporated into an end-to-end and a two-step unsupervised debiasing approach. By tackling the fundamental issue of bias-conflicting training samples’ memorization in learning auxiliary models, typical of this type of technique, our proposed method outperforms the current state-of-the-art in multiple benchmark datasets, demonstrating its potential as a versatile and effective tool for tackling bias in deep learning models. Code is available at https://github.com/Malga-Vision/DiffusingDeBias

Deep neural networks are likely to learn unintended spurious correlations between training data and labels when dealing with biased data, potentially limiting the generalization to unseen samples not presenting the same bias. In this context, model debiasing approaches can be de-vised aiming at reducing the model's dependency on such unwanted correlations, either leveraging the knowledge of bias information or not. In this work, we focus on the latter and more realistic scenario, showing the importance of accurately predicting the bias-conflicting and bias-aligned samples to obtain compelling performance in bias mitigation. On this ground, we propose to conceive the problem of model bias from an out-of-distribution perspective, intro-ducing a new bias identification method based on anomaly detection. We claim that when data is mostly biased, bias-conflicting samples can be regarded as outliers with respect to the bias-aligned distribution in the feature space of a bi-ased model, thus allowing for precisely detecting them with an anomaly detection method. Coupling the proposed bias identification approach with bias-conflicting data upsampling and augmentation in a two-step strategy, we reach state-of-the-art performance on synthetic and real benchmark datasets. Ultimately, our proposed approach shows that the data bias issue does not necessarily require complex debiasing methods, given that an accurate bias identification procedure is defined. Source code is available at https://github.com/Malga-Vision/MoDAD

Planktonic organisms play a pivotal role within aquatic ecosystems, serving as the foundation of the aquatic food chain while also playing a critical role in climate regulation and the production of oxygen. In recent years, the advent of automated systems for capturing in-situ images has led to a huge influx of plankton images, making manual classification impractical. This, at the same time, has opened up opportunities for the application of machine learning and deep learning solutions. This paper undertakes an extensive analysis of the broad range of computer vision techniques and methodologies that have emerged to facilitate the automatic analysis of small- to large-scale datasets containing plankton images. By focusing on different computer vision tasks, we present findings and limitations in order to offer a comprehensive overview of the current state-of-the-art, while also pinpointing the open challenges that demand further research and attention.

Machine learning has significantly impacted the analysis of biological images and is now an important part of many biological data analysis pipelines. A variety of biological and biomedical domain-related tasks is gaining benefit from image analysis and pattern recognition tools developed currently. Applications include diagnostic histopathology, environmental monitoring, synthetic biology, genomics, and proteomics. Particularly in the last decade, several deep learning and advanced computer vision methods such as convolutional neural networks (CNNs), typically trained in a supervised fashion, have started to be largely employed in biological image classification. Moreover, the advancement of automatic acquisition systems has been generating a massive amount of biological data, which requires to be analyzed by domain experts. However, the cost of manual annotation of such data has become a bottleneck, impairing the application of supervised machine learning algorithms. Biological images generally have an intrinsic high variability, whose identity is sometimes hard to assign and strongly dependent on the annotator’s expertise. In this context, a limited number of annotation-free (i.e., unsupervised) learning solutions have been proposed, typically based on hand-crafted features, specifically tailored for a certain biological domain. Nonetheless, a successful unsupervised learning approach must be accurate, and sufficiently robust to deal with different biological domains. This paper aims at providing a viable solution to these issues, proposing an unsupervised learning algorithm based on compressed deep features for image classification. We exploit features extracted from ImageNet pre-trained transformers and CNNs, further compressed with a customized β-Variational AutoEncoder (β-VAE), that we call reconstruction VAE (R-VAE). We test our algorithm on biological images coming from diverse domains characterized by high variability in shape and texture information and acquired with widely differing imaging platforms. Considered image datasets range from multi-cellular organisms (plankton, coral) to sub-cellular organelles (budding yeast vacuoles, human cells’ nuclei, etc.). Our results show that the compressed deep features extracted from different pre-trained vision models establish new unsupervised learning state-of-the-art performances for the investigated datasets.