18,903 research outputs found
Machine Learning Models that Remember Too Much
Machine learning (ML) is becoming a commodity. Numerous ML frameworks and
services are available to data holders who are not ML experts but want to train
predictive models on their data. It is important that ML models trained on
sensitive inputs (e.g., personal images or documents) not leak too much
information about the training data.
We consider a malicious ML provider who supplies model-training code to the
data holder, does not observe the training, but then obtains white- or
black-box access to the resulting model. In this setting, we design and
implement practical algorithms, some of them very similar to standard ML
techniques such as regularization and data augmentation, that "memorize"
information about the training dataset in the model yet the model is as
accurate and predictive as a conventionally trained model. We then explain how
the adversary can extract memorized information from the model.
We evaluate our techniques on standard ML tasks for image classification
(CIFAR10), face recognition (LFW and FaceScrub), and text analysis (20
Newsgroups and IMDB). In all cases, we show how our algorithms create models
that have high predictive power yet allow accurate extraction of subsets of
their training data
Multimodal Visual Concept Learning with Weakly Supervised Techniques
Despite the availability of a huge amount of video data accompanied by
descriptive texts, it is not always easy to exploit the information contained
in natural language in order to automatically recognize video concepts. Towards
this goal, in this paper we use textual cues as means of supervision,
introducing two weakly supervised techniques that extend the Multiple Instance
Learning (MIL) framework: the Fuzzy Sets Multiple Instance Learning (FSMIL) and
the Probabilistic Labels Multiple Instance Learning (PLMIL). The former encodes
the spatio-temporal imprecision of the linguistic descriptions with Fuzzy Sets,
while the latter models different interpretations of each description's
semantics with Probabilistic Labels, both formulated through a convex
optimization algorithm. In addition, we provide a novel technique to extract
weak labels in the presence of complex semantics, that consists of semantic
similarity computations. We evaluate our methods on two distinct problems,
namely face and action recognition, in the challenging and realistic setting of
movies accompanied by their screenplays, contained in the COGNIMUSE database.
We show that, on both tasks, our method considerably outperforms a
state-of-the-art weakly supervised approach, as well as other baselines.Comment: CVPR 201
Many Task Learning with Task Routing
Typical multi-task learning (MTL) methods rely on architectural adjustments
and a large trainable parameter set to jointly optimize over several tasks.
However, when the number of tasks increases so do the complexity of the
architectural adjustments and resource requirements. In this paper, we
introduce a method which applies a conditional feature-wise transformation over
the convolutional activations that enables a model to successfully perform a
large number of tasks. To distinguish from regular MTL, we introduce Many Task
Learning (MaTL) as a special case of MTL where more than 20 tasks are performed
by a single model. Our method dubbed Task Routing (TR) is encapsulated in a
layer we call the Task Routing Layer (TRL), which applied in an MaTL scenario
successfully fits hundreds of classification tasks in one model. We evaluate
our method on 5 datasets against strong baselines and state-of-the-art
approaches.Comment: 8 Pages, 5 Figures, 2 Table
Dynamic Face Video Segmentation via Reinforcement Learning
For real-time semantic video segmentation, most recent works utilised a
dynamic framework with a key scheduler to make online key/non-key decisions.
Some works used a fixed key scheduling policy, while others proposed adaptive
key scheduling methods based on heuristic strategies, both of which may lead to
suboptimal global performance. To overcome this limitation, we model the online
key decision process in dynamic video segmentation as a deep reinforcement
learning problem and learn an efficient and effective scheduling policy from
expert information about decision history and from the process of maximising
global return. Moreover, we study the application of dynamic video segmentation
on face videos, a field that has not been investigated before. By evaluating on
the 300VW dataset, we show that the performance of our reinforcement key
scheduler outperforms that of various baselines in terms of both effective key
selections and running speed. Further results on the Cityscapes dataset
demonstrate that our proposed method can also generalise to other scenarios. To
the best of our knowledge, this is the first work to use reinforcement learning
for online key-frame decision in dynamic video segmentation, and also the first
work on its application on face videos.Comment: CVPR 2020. 300VW with segmentation labels is available at:
https://github.com/mapleandfire/300VW-Mas
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