7,945 research outputs found
Analyzing and Improving Representations with the Soft Nearest Neighbor Loss
We explore and expand the to measure
the of class manifolds in representation space: i.e.,
how close pairs of points from the same class are relative to pairs of points
from different classes. We demonstrate several use cases of the loss. As an
analytical tool, it provides insights into the evolution of class similarity
structures during learning. Surprisingly, we find that
the entanglement of representations of different classes in the hidden layers
is beneficial for discrimination in the final layer, possibly because it
encourages representations to identify class-independent similarity structures.
Maximizing the soft nearest neighbor loss in the hidden layers leads not only
to improved generalization but also to better-calibrated estimates of
uncertainty on outlier data. Data that is not from the training distribution
can be recognized by observing that in the hidden layers, it has fewer than the
normal number of neighbors from the predicted class
Boosting Deep Open World Recognition by Clustering
While convolutional neural networks have brought significant advances in
robot vision, their ability is often limited to closed world scenarios, where
the number of semantic concepts to be recognized is determined by the available
training set. Since it is practically impossible to capture all possible
semantic concepts present in the real world in a single training set, we need
to break the closed world assumption, equipping our robot with the capability
to act in an open world. To provide such ability, a robot vision system should
be able to (i) identify whether an instance does not belong to the set of known
categories (i.e. open set recognition), and (ii) extend its knowledge to learn
new classes over time (i.e. incremental learning). In this work, we show how we
can boost the performance of deep open world recognition algorithms by means of
a new loss formulation enforcing a global to local clustering of class-specific
features. In particular, a first loss term, i.e. global clustering, forces the
network to map samples closer to the class centroid they belong to while the
second one, local clustering, shapes the representation space in such a way
that samples of the same class get closer in the representation space while
pushing away neighbours belonging to other classes. Moreover, we propose a
strategy to learn class-specific rejection thresholds, instead of heuristically
estimating a single global threshold, as in previous works. Experiments on
RGB-D Object and Core50 datasets show the effectiveness of our approach.Comment: IROS/RAL 202
ASK: Adversarial Soft k-Nearest Neighbor Attack and Defense
K-Nearest Neighbor (kNN)-based deep learning methods have been applied to
many applications due to their simplicity and geometric interpretability.
However, the robustness of kNN-based classification models has not been
thoroughly explored and kNN attack strategies are underdeveloped. In this
paper, we propose an Adversarial Soft kNN (ASK) loss to both design more
effective kNN attack strategies and to develop better defenses against them.
Our ASK loss approach has two advantages. First, ASK loss can better
approximate the kNN's probability of classification error than objectives
proposed in previous works. Second, the ASK loss is interpretable: it preserves
the mutual information between the perturbed input and the in-class-reference
data. We use the ASK loss to generate a novel attack method called the
ASK-Attack (ASK-Atk), which shows superior attack efficiency and accuracy
degradation relative to previous kNN attacks. Based on the ASK-Atk, we then
derive an ASK-\underline{Def}ense (ASK-Def) method that optimizes the
worst-case training loss induced by ASK-Atk. Experiments on CIFAR-10 (ImageNet)
show that (i) ASK-Atk achieves () improvement in attack
success rate over previous kNN attacks, and (ii) ASK-Def outperforms the
conventional adversarial training method by () in
terms of robustness improvement
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