44,130 research outputs found
Learning with Biased Complementary Labels
In this paper, we study the classification problem in which we have access to
easily obtainable surrogate for true labels, namely complementary labels, which
specify classes that observations do \textbf{not} belong to. Let and
be the true and complementary labels, respectively. We first model
the annotation of complementary labels via transition probabilities
, where is the number of
classes. Previous methods implicitly assume that , are identical, which is not true in practice because humans are
biased toward their own experience. For example, as shown in Figure 1, if an
annotator is more familiar with monkeys than prairie dogs when providing
complementary labels for meerkats, she is more likely to employ "monkey" as a
complementary label. We therefore reason that the transition probabilities will
be different. In this paper, we propose a framework that contributes three main
innovations to learning with \textbf{biased} complementary labels: (1) It
estimates transition probabilities with no bias. (2) It provides a general
method to modify traditional loss functions and extends standard deep neural
network classifiers to learn with biased complementary labels. (3) It
theoretically ensures that the classifier learned with complementary labels
converges to the optimal one learned with true labels. Comprehensive
experiments on several benchmark datasets validate the superiority of our
method to current state-of-the-art methods.Comment: ECCV 2018 Ora
Generative-Discriminative Complementary Learning
Majority of state-of-the-art deep learning methods are discriminative
approaches, which model the conditional distribution of labels given inputs
features. The success of such approaches heavily depends on high-quality
labeled instances, which are not easy to obtain, especially as the number of
candidate classes increases. In this paper, we study the complementary learning
problem. Unlike ordinary labels, complementary labels are easy to obtain
because an annotator only needs to provide a yes/no answer to a randomly chosen
candidate class for each instance. We propose a generative-discriminative
complementary learning method that estimates the ordinary labels by modeling
both the conditional (discriminative) and instance (generative) distributions.
Our method, we call Complementary Conditional GAN (CCGAN), improves the
accuracy of predicting ordinary labels and can generate high-quality instances
in spite of weak supervision. In addition to the extensive empirical studies,
we also theoretically show that our model can retrieve the true conditional
distribution from the complementarily-labeled data
Scrutinizing and De-Biasing Intuitive Physics with Neural Stethoscopes
Visually predicting the stability of block towers is a popular task in the
domain of intuitive physics. While previous work focusses on prediction
accuracy, a one-dimensional performance measure, we provide a broader analysis
of the learned physical understanding of the final model and how the learning
process can be guided. To this end, we introduce neural stethoscopes as a
general purpose framework for quantifying the degree of importance of specific
factors of influence in deep neural networks as well as for actively promoting
and suppressing information as appropriate. In doing so, we unify concepts from
multitask learning as well as training with auxiliary and adversarial losses.
We apply neural stethoscopes to analyse the state-of-the-art neural network for
stability prediction. We show that the baseline model is susceptible to being
misled by incorrect visual cues. This leads to a performance breakdown to the
level of random guessing when training on scenarios where visual cues are
inversely correlated with stability. Using stethoscopes to promote meaningful
feature extraction increases performance from 51% to 90% prediction accuracy.
Conversely, training on an easy dataset where visual cues are positively
correlated with stability, the baseline model learns a bias leading to poor
performance on a harder dataset. Using an adversarial stethoscope, the network
is successfully de-biased, leading to a performance increase from 66% to 88%
Distribution-Aware Semantics-Oriented Pseudo-label for Imbalanced Semi-Supervised Learning
The capability of the traditional semi-supervised learning (SSL) methods is
far from real-world application since they do not consider (1) class imbalance
and (2) class distribution mismatch between labeled and unlabeled data. This
paper addresses such a relatively under-explored problem, imbalanced
semi-supervised learning, where heavily biased pseudo-labels can harm the model
performance. Interestingly, we find that the semantic pseudo-labels from a
similarity-based classifier in feature space and the traditional pseudo-labels
from the linear classifier show the complementary property. To this end, we
propose a general pseudo-labeling framework to address the bias motivated by
this observation. The key idea is to class-adaptively blend the semantic
pseudo-label to the linear one, depending on the current pseudo-label
distribution. Thereby, the increased semantic pseudo-label component suppresses
the false positives in the majority classes and vice versa. We term the novel
pseudo-labeling framework for imbalanced SSL as Distribution-Aware
Semantics-Oriented (DASO) Pseudo-label. Extensive evaluation on CIFAR10/100-LT
and STL10-LT shows that DASO consistently outperforms both recently proposed
re-balancing methods for label and pseudo-label. Moreover, we demonstrate that
typical SSL algorithms can effectively benefit from unlabeled data with DASO,
especially when (1) class imbalance and (2) class distribution mismatch exist
and even on recent real-world Semi-Aves benchmark.Comment: "Code: https://github.com/ytaek-oh/daso
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