11 research outputs found
Zero Shot Learning with the Isoperimetric Loss
We introduce the isoperimetric loss as a regularization criterion for
learning the map from a visual representation to a semantic embedding, to be
used to transfer knowledge to unknown classes in a zero-shot learning setting.
We use a pre-trained deep neural network model as a visual representation of
image data, a Word2Vec embedding of class labels, and linear maps between the
visual and semantic embedding spaces. However, the spaces themselves are not
linear, and we postulate the sample embedding to be populated by noisy samples
near otherwise smooth manifolds. We exploit the graph structure defined by the
sample points to regularize the estimates of the manifolds by inferring the
graph connectivity using a generalization of the isoperimetric inequalities
from Riemannian geometry to graphs. Surprisingly, this regularization alone,
paired with the simplest baseline model, outperforms the state-of-the-art among
fully automated methods in zero-shot learning benchmarks such as AwA and CUB.
This improvement is achieved solely by learning the structure of the underlying
spaces by imposing regularity.Comment: Accepted to AAAI-2
The Structure Transfer Machine Theory and Applications
Representation learning is a fundamental but challenging problem, especially
when the distribution of data is unknown. We propose a new representation
learning method, termed Structure Transfer Machine (STM), which enables feature
learning process to converge at the representation expectation in a
probabilistic way. We theoretically show that such an expected value of the
representation (mean) is achievable if the manifold structure can be
transferred from the data space to the feature space. The resulting structure
regularization term, named manifold loss, is incorporated into the loss
function of the typical deep learning pipeline. The STM architecture is
constructed to enforce the learned deep representation to satisfy the intrinsic
manifold structure from the data, which results in robust features that suit
various application scenarios, such as digit recognition, image classification
and object tracking. Compared to state-of-the-art CNN architectures, we achieve
the better results on several commonly used benchmarks\footnote{The source code
is available. https://github.com/stmstmstm/stm }
Rethinking Knowledge Graph Propagation for Zero-Shot Learning
Graph convolutional neural networks have recently shown great potential for
the task of zero-shot learning. These models are highly sample efficient as
related concepts in the graph structure share statistical strength allowing
generalization to new classes when faced with a lack of data. However,
multi-layer architectures, which are required to propagate knowledge to distant
nodes in the graph, dilute the knowledge by performing extensive Laplacian
smoothing at each layer and thereby consequently decrease performance. In order
to still enjoy the benefit brought by the graph structure while preventing
dilution of knowledge from distant nodes, we propose a Dense Graph Propagation
(DGP) module with carefully designed direct links among distant nodes. DGP
allows us to exploit the hierarchical graph structure of the knowledge graph
through additional connections. These connections are added based on a node's
relationship to its ancestors and descendants. A weighting scheme is further
used to weigh their contribution depending on the distance to the node to
improve information propagation in the graph. Combined with finetuning of the
representations in a two-stage training approach our method outperforms
state-of-the-art zero-shot learning approaches.Comment: The first two authors contributed equally. Code at
https://github.com/cyvius96/adgpm. To appear in CVPR 201