17 research outputs found
Class Adaptive Network Calibration
Recent studies have revealed that, beyond conventional accuracy, calibration
should also be considered for training modern deep neural networks. To address
miscalibration during learning, some methods have explored different penalty
functions as part of the learning objective, alongside a standard
classification loss, with a hyper-parameter controlling the relative
contribution of each term. Nevertheless, these methods share two major
drawbacks: 1) the scalar balancing weight is the same for all classes,
hindering the ability to address different intrinsic difficulties or imbalance
among classes; and 2) the balancing weight is usually fixed without an adaptive
strategy, which may prevent from reaching the best compromise between accuracy
and calibration, and requires hyper-parameter search for each application. We
propose Class Adaptive Label Smoothing (CALS) for calibrating deep networks,
which allows to learn class-wise multipliers during training, yielding a
powerful alternative to common label smoothing penalties. Our method builds on
a general Augmented Lagrangian approach, a well-established technique in
constrained optimization, but we introduce several modifications to tailor it
for large-scale, class-adaptive training. Comprehensive evaluation and multiple
comparisons on a variety of benchmarks, including standard and long-tailed
image classification, semantic segmentation, and text classification,
demonstrate the superiority of the proposed method. The code is available at
https://github.com/by-liu/CALS.Comment: Code: https://github.com/by-liu/CAL
Deep weakly-supervised learning methods for classification and localization in histology images: a survey
Using state-of-the-art deep learning models for cancer diagnosis presents
several challenges related to the nature and availability of labeled histology
images. In particular, cancer grading and localization in these images normally
relies on both image- and pixel-level labels, the latter requiring a costly
annotation process. In this survey, deep weakly-supervised learning (WSL)
models are investigated to identify and locate diseases in histology images,
without the need for pixel-level annotations. Given training data with global
image-level labels, these models allow to simultaneously classify histology
images and yield pixel-wise localization scores, thereby identifying the
corresponding regions of interest (ROI). Since relevant WSL models have mainly
been investigated within the computer vision community, and validated on
natural scene images, we assess the extent to which they apply to histology
images which have challenging properties, e.g. very large size, similarity
between foreground/background, highly unstructured regions, stain
heterogeneity, and noisy/ambiguous labels. The most relevant models for deep
WSL are compared experimentally in terms of accuracy (classification and
pixel-wise localization) on several public benchmark histology datasets for
breast and colon cancer -- BACH ICIAR 2018, BreaKHis, CAMELYON16, and GlaS.
Furthermore, for large-scale evaluation of WSL models on histology images, we
propose a protocol to construct WSL datasets from Whole Slide Imaging. Results
indicate that several deep learning models can provide a high level of
classification accuracy, although accurate pixel-wise localization of cancer
regions remains an issue for such images. Code is publicly available.Comment: 35 pages, 18 figure
Proximal Splitting Adversarial Attacks for Semantic Segmentation
Classification has been the focal point of research on adversarial attacks,
but only a few works investigate methods suited to denser prediction tasks,
such as semantic segmentation. The methods proposed in these works do not
accurately solve the adversarial segmentation problem and, therefore, are
overoptimistic in terms of size of the perturbations required to fool models.
Here, we propose a white-box attack for these models based on a proximal
splitting to produce adversarial perturbations with much smaller ,
, or norms. Our attack can handle large numbers of
constraints within a nonconvex minimization framework via an Augmented
Lagrangian approach, coupled with adaptive constraint scaling and masking
strategies. We demonstrate that our attack significantly outperforms previously
proposed ones, as well as classification attacks that we adapted for
segmentation, providing a first comprehensive benchmark for this dense task.
Our results push current limits concerning robustness evaluations in
segmentation tasks.Comment: Code available at:
https://github.com/jeromerony/alma_prox_segmentatio
Proximal Splitting Adversarial Attack for Semantic Segmentation
International audienceClassification has been the focal point of research on adversarial attacks, but only a few works investigate methods suited to denser prediction tasks, such as semantic segmentation. The methods proposed in these works do not accurately solve the adversarial segmentation problem and, therefore, overestimate the size of the perturbations required to fool models. Here, we propose a white-box attack for these models based on a proximal splitting to produce adversarial perturbations with much smaller \ell_\infty norms. Our attack can handle large numbers of constraints within a nonconvex minimization framework via an Augmented Lagrangian approach, coupled with adaptive constraint scaling and masking strategies. We demonstrate that our attack significantly outperforms previously proposed ones, as well as classification attacks that we adapted for segmentation, providing a first comprehensive benchmark for this dense task
Transductive Information Maximization For Few-Shot Learning
International audienceWe introduce Transductive Infomation Maximization (TIM) for few-shot learning. Our method maximizes the mutual information between the query features and their label predictions for a given few-shot task, in conjunction with a supervision loss based on the support set. Furthermore, we propose a new alternating-direction solver for our mutual-information loss, which substantially speeds up transductiveinference convergence over gradient-based optimization, while yielding similar accuracy. TIM inference is modular: it can be used on top of any base-training feature extractor. Following standard transductive few-shot settings, our comprehensive experiments 2 demonstrate that TIM outperforms state-of-the-art methods significantly across various datasets and networks, while used on top of a fixed feature extractor trained with simple cross-entropy on the base classes, without resorting to complex meta-learning schemes. It consistently brings between 2% and 5% improvement in accuracy over the best performing method, not only on all the well-established few-shot benchmarks but also on more challenging scenarios, with domain shifts and larger numbers of classes