An Overview of Deep Semi-Supervised Learning

Deep neural networks demonstrated their ability to provide remarkable performances on a wide range of supervised learning tasks (e.g., image classification) when trained on extensive collections of labeled data (e.g., ImageNet). However, creating such large datasets requires a considerable amount of resources, time, and effort. Such resources may not be available in many practical cases, limiting the adoption and the application of many deep learning methods. In a search for more data-efficient deep learning methods to overcome the need for large annotated datasets, there is a rising research interest in semi-supervised learning and its applications to deep neural networks to reduce the amount of labeled data required, by either developing novel methods or adopting existing semi-supervised learning frameworks for a deep learning setting. In this paper, we provide a comprehensive overview of deep semi-supervised learning, starting with an introduction to the field, followed by a summarization of the dominant semi-supervised approaches in deep learning.Comment: Preprin

Spatial Contrastive Learning for Few-Shot Classification

Existing few-shot classification methods rely to some degree on the cross-entropy (CE) loss to learn transferable representations that facilitate the test time adaptation to unseen classes with limited data. However, the CE loss has several shortcomings, e.g., inducing representations with excessive discrimination towards seen classes, which reduces their transferability to unseen classes and results in sub-optimal generalization. In this work, we explore contrastive learning as an additional auxiliary training objective, acting as a data-dependent regularizer to promote more general and transferable features. Instead of using the standard contrastive objective, which suppresses local discriminative features, we propose a novel attention-based spatial contrastive objective to learn locally discriminative and class-agnostic features. With extensive experiments, we show that the proposed method outperforms state-of-the-art approaches, confirming the importance of learning good and transferable embeddings for few-shot learning.Comment: Preprin

Black Box Few-Shot Adaptation for Vision-Language models

Vision-Language (V-L) models trained with contrastive learning to align the visual and language modalities have been shown to be strong few-shot learners. Soft prompt learning is the method of choice for few-shot downstream adaptation aiming to bridge the modality gap caused by the distribution shift induced by the new domain. While parameter-efficient, prompt learning still requires access to the model weights and can be computationally infeasible for large models with billions of parameters. To address these shortcomings, in this work, we describe a black-box method for V-L few-shot adaptation that (a) operates on pre-computed image and text features and hence works without access to the model's weights, (b) it is orders of magnitude faster at training time, (c) it is amenable to both supervised and unsupervised training, and (d) it can be even used to align image and text features computed from uni-modal models. To achieve this, we propose Linear Feature Alignment (LFA), a simple linear approach for V-L re-alignment in the target domain. LFA is initialized from a closed-form solution to a least-squares problem and then it is iteratively updated by minimizing a re-ranking loss. Despite its simplicity, our approach can even surpass soft-prompt learning methods as shown by extensive experiments on 11 image and 2 video datasets.Comment: Published at ICCV 202

Apprentissage avec peu de données étiquetées

Depuis ses débuts, l'objectif de l'intelligence artificielle est de concevoir des systèmes capables d'apprendre aussi efficacement que les humains pour résoudre ou aider à résoudre des problèmes difficiles qui nécessitent une certaine forme d'intelligence humaine. La discipline a connu récemment un essor spectaculaire grâce aux réseaux de neurones profonds et ses extensions qui ont montré des performances sur tout un ensemble de tâches jusqu'alors considérées complexes. Cependant, ce paradigme dominant nécessite une grande quantité de données étiquetées, qui sont souvent coûteuses et difficiles à acquérir. Ces données peuvent également contenir des biais cachés et des erreurs d'annotation, ce qui limite l'application de tels systèmes dans de nombreux domaines. Pourtant, les humains font preuve d'une remarquable capacité à apprendre efficacement dans de nouveaux et divers contextes, en tirant en grande parti de leur expérience à s'adapter à de nouveaux cas et acquérir rapidement de nouvelles compétences. Cette divergence soulève une question évidente : pouvons-nous concevoir des systèmes dotés de capacités similaires ? Dans cette thèse, notre objectif est de développer des algorithmes d'apprentissage efficaces avec une quantité limitée d'étiquettes pour résoudre de diverses tâches pour différentes modalités. A cette fin, cette thèse couvre des travaux qui : i) développent des méthodes d'apprentissage pour des paradigmes avec différents degrés de supervision, ii) présentent des résultats pour différentes modalités, notamment l'image et le texte, et iii) qui gèrent différentes tâches.Since its inception, the north star of artificial intelligence was to design systems capable of learning as efficiently (i.e. with limited training signal) and effectively (i.e. demonstrating good performances) as humans to solve challenging problems that require human-like intelligence. Deep neural networks and the collection of popular deep learning ingredients used to produce systems usable in the real world, such as optimization algorithms, novel architectures, objective functions, and large annotated datasets, have shown remarkable performances across various tasks in recent years. However, this dominant paradigm requires a large amount of fully labeled data, which is often expensive and difficult to acquire. It might also contain annotation errors and hidden biases, which limits the applicability and adoption of such systems. Yet humans demonstrate a remarkable ability to learn effectively across diverse settings, using limited supervision and leveraging prior experience to adapt to novel cases and gain new skills quickly. This discrepancy raises an obvious question, can we design systems with similar capabilities? In this thesis, we aim to develop label-efficient learning algorithms that are effective with a limited or no amount of annotated examples for various tasks over different modalities and multiple levels of abstraction. To this end, this thesis cover works that: i) develop learning methods for paradigms with varying degrees of supervision, ii) present results for different modalities, notably vision, and text, and iii) different tasks across various levels of abstraction (e.g., image level and pixel level). We hope these works can help further advance the state of the field and aid in developing systems capable of learning efficiently and adapting effectively across a wide range of environments

Learning with Limited Labeled Data

Since its inception, the north star of artificial intelligence was to design systems capable of learning as efficiently (i.e. with limited training signal) and effectively (i.e. demonstrating good performances) as humans to solve challenging problems that require human-like intelligence. Deep neural networks and the collection of popular deep learning ingredients used to produce systems usable in the real world, such as optimization algorithms, novel architectures, objective functions, and large annotated datasets, have shown remarkable performances across various tasks in recent years. However, this dominant paradigm requires a large amount of fully labeled data, which is often expensive and difficult to acquire. It might also contain annotation errors and hidden biases, which limits the applicability and adoption of such systems. Yet humans demonstrate a remarkable ability to learn effectively across diverse settings, using limited supervision and leveraging prior experience to adapt to novel cases and gain new skills quickly. This discrepancy raises an obvious question, can we design systems with similar capabilities? In this thesis, we aim to develop label-efficient learning algorithms that are effective with a limited or no amount of annotated examples for various tasks over different modalities and multiple levels of abstraction. To this end, this thesis cover works that: i) develop learning methods for paradigms with varying degrees of supervision, ii) present results for different modalities, notably vision, and text, and iii) different tasks across various levels of abstraction (e.g., image level and pixel level). We hope these works can help further advance the state of the field and aid in developing systems capable of learning efficiently and adapting effectively across a wide range of environments.Depuis ses débuts, l'objectif de l'intelligence artificielle est de concevoir des systèmes capables d'apprendre aussi efficacement que les humains pour résoudre ou aider à résoudre des problèmes difficiles qui nécessitent une certaine forme d'intelligence humaine. La discipline a connu récemment un essor spectaculaire grâce aux réseaux de neurones profonds et ses extensions qui ont montré des performances sur tout un ensemble de tâches jusqu'alors considérées complexes. Cependant, ce paradigme dominant nécessite une grande quantité de données étiquetées, qui sont souvent coûteuses et difficiles à acquérir. Ces données peuvent également contenir des biais cachés et des erreurs d'annotation, ce qui limite l'application de tels systèmes dans de nombreux domaines. Pourtant, les humains font preuve d'une remarquable capacité à apprendre efficacement dans de nouveaux et divers contextes, en tirant en grande parti de leur expérience à s'adapter à de nouveaux cas et acquérir rapidement de nouvelles compétences. Cette divergence soulève une question évidente : pouvons-nous concevoir des systèmes dotés de capacités similaires ? Dans cette thèse, notre objectif est de développer des algorithmes d'apprentissage efficaces avec une quantité limitée d'étiquettes pour résoudre de diverses tâches pour différentes modalités. A cette fin, cette thèse couvre des travaux qui : i) développent des méthodes d'apprentissage pour des paradigmes avec différents degrés de supervision, ii) présentent des résultats pour différentes modalités, notamment l'image et le texte, et iii) qui gèrent différentes tâches

Learning with Limited Labeled Data

Since its inception, the north star of artificial intelligence was to design systems capable of learning as efficiently (i.e. with limited training signal) and effectively (i.e. demonstrating good performances) as humans to solve challenging problems that require human-like intelligence. Deep neural networks and the collection of popular deep learning ingredients used to produce systems usable in the real world, such as optimization algorithms, novel architectures, objective functions, and large annotated datasets, have shown remarkable performances across various tasks in recent years. However, this dominant paradigm requires a large amount of fully labeled data, which is often expensive and difficult to acquire. It might also contain annotation errors and hidden biases, which limits the applicability and adoption of such systems. Yet humans demonstrate a remarkable ability to learn effectively across diverse settings, using limited supervision and leveraging prior experience to adapt to novel cases and gain new skills quickly. This discrepancy raises an obvious question, can we design systems with similar capabilities? In this thesis, we aim to develop label-efficient learning algorithms that are effective with a limited or no amount of annotated examples for various tasks over different modalities and multiple levels of abstraction. To this end, this thesis cover works that: i) develop learning methods for paradigms with varying degrees of supervision, ii) present results for different modalities, notably vision, and text, and iii) different tasks across various levels of abstraction (e.g., image level and pixel level). We hope these works can help further advance the state of the field and aid in developing systems capable of learning efficiently and adapting effectively across a wide range of environments.Depuis ses débuts, l'objectif de l'intelligence artificielle est de concevoir des systèmes capables d'apprendre aussi efficacement que les humains pour résoudre ou aider à résoudre des problèmes difficiles qui nécessitent une certaine forme d'intelligence humaine. La discipline a connu récemment un essor spectaculaire grâce aux réseaux de neurones profonds et ses extensions qui ont montré des performances sur tout un ensemble de tâches jusqu'alors considérées complexes. Cependant, ce paradigme dominant nécessite une grande quantité de données étiquetées, qui sont souvent coûteuses et difficiles à acquérir. Ces données peuvent également contenir des biais cachés et des erreurs d'annotation, ce qui limite l'application de tels systèmes dans de nombreux domaines. Pourtant, les humains font preuve d'une remarquable capacité à apprendre efficacement dans de nouveaux et divers contextes, en tirant en grande parti de leur expérience à s'adapter à de nouveaux cas et acquérir rapidement de nouvelles compétences. Cette divergence soulève une question évidente : pouvons-nous concevoir des systèmes dotés de capacités similaires ? Dans cette thèse, notre objectif est de développer des algorithmes d'apprentissage efficaces avec une quantité limitée d'étiquettes pour résoudre de diverses tâches pour différentes modalités. A cette fin, cette thèse couvre des travaux qui : i) développent des méthodes d'apprentissage pour des paradigmes avec différents degrés de supervision, ii) présentent des résultats pour différentes modalités, notamment l'image et le texte, et iii) qui gèrent différentes tâches

Semi-Supervised Semantic Segmentation with Cross-Consistency Training

International audienceIn this paper, we present a novel cross-consistency based semi-supervised approach for semantic segmentation. Consistency training has proven to be a powerful semisupervised learning framework for leveraging unlabeled data under the cluster assumption, in which the decision boundary should lie in low density regions. In this work, we first observe that for semantic segmentation, the low density regions are more apparent within the hidden representations than within the inputs. We thus propose crossconsistency training, where an invariance of the predictions is enforced over different perturbations applied to the outputs of the encoder. Concretely, a shared encoder and a main decoder are trained in a supervised manner using the available labeled examples. To leverage the unlabeled examples, we enforce a consistency between the main decoder predictions and those of the auxiliary decoders, taking as inputs different perturbed versions of the encoder's output, and consequently, improving the encoder's representations. The proposed method is simple and can easily be extended to use additional training signal, such as image-level labels or pixel-level labels across different domains. We perform an ablation study to tease apart the effectiveness of each component, and conduct extensive experiments to demonstrate that our method achieves stateof-the-art results in several datasets

Larvicidal Activity of Nerium oleander against Larvae West Nile Vector Mosquito Culex pipiens (Diptera: Culicidae)

Background. Outbreaks of the West Nile virus infection were reported in Morocco in 1996, 2003, and 2010. Culex pipiens was strongly suspected as the vector responsible for transmission. In the North center of Morocco, this species has developed resistance to synthetic insecticides. There is an urgent need to find alternatives to the insecticides as natural biocides. Objective. In this work, the insecticidal activity of the extract of the local plant Nerium oleander, which has never been tested before in the North center of Morocco, was studied on larval stages 3 and 4 of Culex pipiens. Methods. Biological tests were realized according to a methodology inspired from standard World Health Organization protocol. The mortality values were determined after 24 h of exposure and LC50 and LC90 values were calculated. Results. The extract had toxic effects on the larvae of culicid mosquitoes. The ethanolic extract of Nerium oleander applied against the larvae of Culex pipiens has given the lethal concentrations LC50 and LC90 in the order of 57.57 mg/mL and 166.35 mg/mL, respectively. Conclusion. This investigation indicates that N. oleander could serve as a potential larvicidal, effective natural biocide against mosquito larvae, particularly Culex pipiens