140 research outputs found
Advancing Biomedicine with Graph Representation Learning: Recent Progress, Challenges, and Future Directions
Graph representation learning (GRL) has emerged as a pivotal field that has
contributed significantly to breakthroughs in various fields, including
biomedicine. The objective of this survey is to review the latest advancements
in GRL methods and their applications in the biomedical field. We also
highlight key challenges currently faced by GRL and outline potential
directions for future research.Comment: Accepted by 2023 IMIA Yearbook of Medical Informatic
Bias in Deep Learning and Applications to Face Analysis
Deep learning has fostered the progress in the field of face analysis, resulting in the integration of these models in multiple aspects of society. Even though the majority of research has focused on optimizing standard evaluation metrics, recent work has exposed the bias of such algorithms as well as the dangers of their unaccountable utilization.n this thesis, we explore the bias of deep learning models in the discriminative and the generative setting. We begin by investigating the bias of face analysis models with regards to different demographics. To this end, we collect KANFace, a large-scale video and image dataset of faces captured ``in-the-wild’'. The rich set of annotations allows us to expose the demographic bias of deep learning models, which we mitigate by utilizing adversarial learning to debias the deep representations. Furthermore, we explore neural augmentation as a strategy towards training fair classifiers. We propose a style-based multi-attribute transfer framework that is able to synthesize photo-realistic faces of the underrepresented demographics. This is achieved by introducing a multi-attribute extension to Adaptive Instance Normalisation that captures the multiplicative interactions between the representations of different attributes. Focusing on bias in gender recognition, we showcase the efficacy of the framework in training classifiers that are more fair compared to generative and fairness-aware methods.In the second part, we focus on bias in deep generative models. In particular, we start by studying the generalization of generative models on images of unseen attribute combinations. To this end, we extend the conditional Variational Autoencoder by introducing a multilinear conditioning framework. The proposed method is able to synthesize unseen attribute combinations by modeling the multiplicative interactions between the attributes. Lastly, in order to control protected attributes, we investigate controlled image generation without training on a labelled dataset. We leverage pre-trained Generative Adversarial Networks that are trained in an unsupervised fashion and exploit the clustering that occurs in the representation space of intermediate layers of the generator. We show that these clusters capture semantic attribute information and condition image synthesis on the cluster assignment using Implicit Maximum Likelihood Estimation.Open Acces
Information Maximization for Extreme Pose Face Recognition
In this paper, we seek to draw connections between the frontal and profile
face images in an abstract embedding space. We exploit this connection using a
coupled-encoder network to project frontal/profile face images into a common
latent embedding space. The proposed model forces the similarity of
representations in the embedding space by maximizing the mutual information
between two views of the face. The proposed coupled-encoder benefits from three
contributions for matching faces with extreme pose disparities. First, we
leverage our pose-aware contrastive learning to maximize the mutual information
between frontal and profile representations of identities. Second, a memory
buffer, which consists of latent representations accumulated over past
iterations, is integrated into the model so it can refer to relatively much
more instances than the mini-batch size. Third, a novel pose-aware adversarial
domain adaptation method forces the model to learn an asymmetric mapping from
profile to frontal representation. In our framework, the coupled-encoder learns
to enlarge the margin between the distribution of genuine and imposter faces,
which results in high mutual information between different views of the same
identity. The effectiveness of the proposed model is investigated through
extensive experiments, evaluations, and ablation studies on four benchmark
datasets, and comparison with the compelling state-of-the-art algorithms.Comment: INTERNATIONAL JOINT CONFERENCE ON BIOMETRICS (IJCB 2022
A Survey of Face Recognition
Recent years witnessed the breakthrough of face recognition with deep
convolutional neural networks. Dozens of papers in the field of FR are
published every year. Some of them were applied in the industrial community and
played an important role in human life such as device unlock, mobile payment,
and so on. This paper provides an introduction to face recognition, including
its history, pipeline, algorithms based on conventional manually designed
features or deep learning, mainstream training, evaluation datasets, and
related applications. We have analyzed and compared state-of-the-art works as
many as possible, and also carefully designed a set of experiments to find the
effect of backbone size and data distribution. This survey is a material of the
tutorial named The Practical Face Recognition Technology in the Industrial
World in the FG2023
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Content-Style Decomposition: Representation Discovery and Applications
Content-style decompositions, or CSDs, decompose entities into content, defined by the entity's class, and style, defined as the remaining within-class variation. Content is typically defined in terms of some task. For example, in a face recognition task, identity is the content; in an emotion recognition task, expression is the content. CSDs have many applications: they can provide insight into domains where we have little prior knowledge of the sources of within- and between-class variation, and content-style recombinations are interesting as a creative exercise or for data set augmentation. Our approach is to decompose CSD discovery into two sub-problems: (1) to find useful representations of content that capture the class structure of the domain, and (2) to use those content-representations to discover CSDs. We make contributions to both sub-problems. First, we propose the F-statistic loss, a new method for discovering content representations that uses statistics of class separation on isolated embedding dimensions within a minibatch to determine when to terminate training. In addition to state-of-the-art performance on few-shot learning, we find that the method leads to factorial (also known as disentangled) representations of content when applied with a novel form of weak supervision. Previous work on disentangling is either unsupervised or uses a factor-aware oracle, which provides similar/dissimilar judgments with respect to a named attribute/factor. We explore an intermediate form of supervision, an unnamed-factor oracle, which provides similarity judgments with respect to a random unnamed factor. We demonstrate that the F-statistic loss leads to better disentangling when compared with other deep-embeddings losses and β-VAE, a state-of-the-art unsupervised disentangling method. Second, we introduce a new loss for discovering CSDs that can arbitrarily recombine content and style, called leakage filtering. In contrast to previous research which attempts to separate content and style in two different representation vectors, leakage filtering allows for imperfectly disentangled representations but ensures that residual content information will not leak out of the style representation and vice versa. Leakage filtering is also distinguished by its ability to operate on novel content-classes and by its lack of dependency on style labels for training. The recombined images produced are of high quality and can be used to augment datasets for few-shot learning tasks, yielding significant generalization improvements
Latent Disentanglement for the Analysis and Generation of Digital Human Shapes
Analysing and generating digital human shapes is crucial for a wide variety of applications ranging from movie production to healthcare. The most common approaches for the analysis and generation of digital human shapes involve the creation of statistical shape models. At the heart of these techniques is the definition of a mapping between shapes and a low-dimensional representation. However, making these representations interpretable is still an open challenge. This thesis explores latent disentanglement as a powerful technique to make the latent space of geometric deep learning based statistical shape models more structured and interpretable. In particular, it introduces two novel techniques to disentangle the latent representation of variational autoencoders and generative adversarial networks with respect to the local shape attributes characterising the identity of the generated body and head meshes. This work was inspired by a shape completion framework that was proposed as a viable alternative to intraoperative registration in minimally invasive surgery of the liver. In addition, one of these methods for latent disentanglement was also applied to plastic surgery, where it was shown to improve the diagnosis of craniofacial syndromes and aid surgical planning
ITI-GEN: Inclusive Text-to-Image Generation
Text-to-image generative models often reflect the biases of the training
data, leading to unequal representations of underrepresented groups. This study
investigates inclusive text-to-image generative models that generate images
based on human-written prompts and ensure the resulting images are uniformly
distributed across attributes of interest. Unfortunately, directly expressing
the desired attributes in the prompt often leads to sub-optimal results due to
linguistic ambiguity or model misrepresentation. Hence, this paper proposes a
drastically different approach that adheres to the maxim that "a picture is
worth a thousand words". We show that, for some attributes, images can
represent concepts more expressively than text. For instance, categories of
skin tones are typically hard to specify by text but can be easily represented
by example images. Building upon these insights, we propose a novel approach,
ITI-GEN, that leverages readily available reference images for Inclusive
Text-to-Image GENeration. The key idea is learning a set of prompt embeddings
to generate images that can effectively represent all desired attribute
categories. More importantly, ITI-GEN requires no model fine-tuning, making it
computationally efficient to augment existing text-to-image models. Extensive
experiments demonstrate that ITI-GEN largely improves over state-of-the-art
models to generate inclusive images from a prompt. Project page:
https://czhang0528.github.io/iti-gen.Comment: Accepted to ICCV 2023 (Oral Presentation
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