14 research outputs found
A hypothesize-and-verify framework for Text Recognition using Deep Recurrent Neural Networks
Deep LSTM is an ideal candidate for text recognition. However text
recognition involves some initial image processing steps like segmentation of
lines and words which can induce error to the recognition system. Without
segmentation, learning very long range context is difficult and becomes
computationally intractable. Therefore, alternative soft decisions are needed
at the pre-processing level. This paper proposes a hybrid text recognizer using
a deep recurrent neural network with multiple layers of abstraction and long
range context along with a language model to verify the performance of the deep
neural network. In this paper we construct a multi-hypotheses tree architecture
with candidate segments of line sequences from different segmentation
algorithms at its different branches. The deep neural network is trained on
perfectly segmented data and tests each of the candidate segments, generating
unicode sequences. In the verification step, these unicode sequences are
validated using a sub-string match with the language model and best first
search is used to find the best possible combination of alternative hypothesis
from the tree structure. Thus the verification framework using language models
eliminates wrong segmentation outputs and filters recognition errors
On discovering and learning structure under limited supervision
Les formes, les surfaces, les événements et les objets (vivants et non vivants) constituent le monde. L'intelligence des agents naturels, tels que les humains, va au-delà de la simple reconnaissance de formes. Nous excellons à construire des représentations et à distiller des connaissances pour comprendre et déduire la structure du monde. Spécifiquement, le développement de telles capacités de raisonnement peut se produire même avec une supervision limitée.
D'autre part, malgré son développement phénoménal, les succès majeurs de l'apprentissage automatique, en particulier des modèles d'apprentissage profond, se situent principalement dans les tâches qui ont accès à de grands ensembles de données annotées. Dans cette thèse, nous proposons de nouvelles solutions pour aider à combler cette lacune en permettant aux modèles d'apprentissage automatique d'apprendre la structure et de permettre un raisonnement efficace en présence de tâches faiblement supervisés.
Le thème récurrent de la thèse tente de s'articuler autour de la question « Comment un système perceptif peut-il apprendre à organiser des informations sensorielles en connaissances utiles sous une supervision limitée ? » Et il aborde les thèmes de la géométrie, de la composition et des associations dans quatre articles distincts avec des applications à la vision par ordinateur (CV) et à l'apprentissage par renforcement (RL).
Notre première contribution ---Pix2Shape---présente une approche basée sur l'analyse par synthèse pour la perception. Pix2Shape exploite des modèles génératifs probabilistes pour apprendre des représentations 3D à partir d'images 2D uniques. Le formalisme qui en résulte nous offre une nouvelle façon de distiller l'information d'une scène ainsi qu'une représentation puissantes des images. Nous y parvenons en augmentant l'apprentissage profond non supervisé avec des biais inductifs basés sur la physique pour décomposer la structure causale des images en géométrie, orientation, pose, réflectance et éclairage.
Notre deuxième contribution ---MILe--- aborde les problèmes d'ambiguïté dans les ensembles de données à label unique tels que ImageNet. Il est souvent inapproprié de décrire une image avec un seul label lorsqu'il est composé de plus d'un objet proéminent. Nous montrons que l'intégration d'idées issues de la littérature linguistique cognitive et l'imposition de biais inductifs appropriés aident à distiller de multiples descriptions possibles à l'aide d'ensembles de données aussi faiblement étiquetés.
Ensuite, nous passons au paradigme d'apprentissage par renforcement, et considérons un agent interagissant avec son environnement sans signal de récompense. Notre troisième contribution ---HaC--- est une approche non supervisée basée sur la curiosité pour apprendre les associations entre les modalités visuelles et tactiles. Cela aide l'agent à explorer l'environnement de manière autonome et à utiliser davantage ses connaissances pour s'adapter aux tâches en aval. La supervision dense des récompenses n'est pas toujours disponible (ou n'est pas facile à concevoir), dans de tels cas, une exploration efficace est utile pour générer un comportement significatif de manière auto-supervisée.
Pour notre contribution finale, nous abordons l'information limitée contenue dans les représentations obtenues par des agents RL non supervisés. Ceci peut avoir un effet néfaste sur la performance des agents lorsque leur perception est basée sur des images de haute dimension. Notre approche a base de modèles combine l'exploration et la planification sans récompense pour affiner efficacement les modèles pré-formés non supervisés, obtenant des résultats comparables à un agent entraîné spécifiquement sur ces tâches. Il s'agit d'une étape vers la création d'agents capables de généraliser rapidement à plusieurs tâches en utilisant uniquement des images comme perception.Shapes, surfaces, events, and objects (living and non-living) constitute the world. The intelligence of natural agents, such as humans is beyond pattern recognition. We excel at building representations and distilling knowledge to understand and infer the structure of the world. Critically, the development of such reasoning capabilities can occur even with limited supervision.
On the other hand, despite its phenomenal development, the major successes of machine learning, in particular, deep learning models are primarily in tasks that have access to large annotated datasets. In this dissertation, we propose novel solutions to help address this gap by enabling machine learning models to learn the structure and enable effective reasoning in the presence of weakly supervised settings.
The recurring theme of the thesis tries to revolve around the question of "How can a perceptual system learn to organize sensory information into useful knowledge under limited supervision?" And it discusses the themes of geometry, compositions, and associations in four separate articles with applications to computer vision (CV) and reinforcement learning (RL).
Our first contribution ---Pix2Shape---presents an analysis-by-synthesis based approach(also referred to as inverse graphics) for perception. Pix2Shape leverages probabilistic generative models to learn 3D-aware representations from single 2D images. The resulting formalism allows us to perform a novel view synthesis of a scene and produce powerful representations of images. We achieve this by augmenting unsupervised learning with physically based inductive biases to decompose a scene structure into geometry, pose, reflectance and lighting.
Our Second contribution ---MILe--- addresses the ambiguity issues in single-labeled datasets such as ImageNet. It is often inappropriate to describe an image with a single label when it is composed of more than one prominent object. We show that integrating ideas from Cognitive linguistic literature and imposing appropriate inductive biases helps in distilling multiple possible descriptions using such weakly labeled datasets.
Next, moving into the RL setting, we consider an agent interacting with its environment without a reward signal. Our third Contribution ---HaC--- is a curiosity based unsupervised approach to learning associations between visual and tactile modalities. This aids the agent to explore the environment in an analogous self-guided fashion and further use this knowledge to adapt to downstream tasks.
In the absence of reward supervision, intrinsic movitivation is useful to generate meaningful behavior in a self-supervised manner.
In our final contribution, we address the representation learning bottleneck in unsupervised RL agents that has detrimental effect on the performance on high-dimensional pixel based inputs. Our model-based approach combines reward-free exploration and planning to efficiently fine-tune unsupervised pre-trained models, achieving comparable results to task-specific baselines. This is a step towards building agents that can generalize quickly on more than a single task using image inputs alone
Adversarial Generation of Natural Language
Generative Adversarial Networks (GANs) have gathered a lot of attention from
the computer vision community, yielding impressive results for image
generation. Advances in the adversarial generation of natural language from
noise however are not commensurate with the progress made in generating images,
and still lag far behind likelihood based methods. In this paper, we take a
step towards generating natural language with a GAN objective alone. We
introduce a simple baseline that addresses the discrete output space problem
without relying on gradient estimators and show that it is able to achieve
state-of-the-art results on a Chinese poem generation dataset. We present
quantitative results on generating sentences from context-free and
probabilistic context-free grammars, and qualitative language modeling results.
A conditional version is also described that can generate sequences conditioned
on sentence characteristics.Comment: 11 pages, 3 figures, 5 table
Choreographer: Learning and Adapting Skills in Imagination
Unsupervised skill learning aims to learn a rich repertoire of behaviors
without external supervision, providing artificial agents with the ability to
control and influence the environment. However, without appropriate knowledge
and exploration, skills may provide control only over a restricted area of the
environment, limiting their applicability. Furthermore, it is unclear how to
leverage the learned skill behaviors for adapting to downstream tasks in a
data-efficient manner. We present Choreographer, a model-based agent that
exploits its world model to learn and adapt skills in imagination. Our method
decouples the exploration and skill learning processes, being able to discover
skills in the latent state space of the model. During adaptation, the agent
uses a meta-controller to evaluate and adapt the learned skills efficiently by
deploying them in parallel in imagination. Choreographer is able to learn
skills both from offline data, and by collecting data simultaneously with an
exploration policy. The skills can be used to effectively adapt to downstream
tasks, as we show in the URL benchmark, where we outperform previous approaches
from both pixels and states inputs. The learned skills also explore the
environment thoroughly, finding sparse rewards more frequently, as shown in
goal-reaching tasks from the DMC Suite and Meta-World. Project website:
https://skillchoreographer.github.io
Equivariant Adaptation of Large Pretrained Models
Equivariant networks are specifically designed to ensure consistent behavior
with respect to a set of input transformations, leading to higher sample
efficiency and more accurate and robust predictions. However, redesigning each
component of prevalent deep neural network architectures to achieve chosen
equivariance is a difficult problem and can result in a computationally
expensive network during both training and inference. A recently proposed
alternative towards equivariance that removes the architectural constraints is
to use a simple canonicalization network that transforms the input to a
canonical form before feeding it to an unconstrained prediction network. We
show here that this approach can effectively be used to make a large pretrained
network equivariant. However, we observe that the produced canonical
orientations can be misaligned with those of the training distribution,
hindering performance. Using dataset-dependent priors to inform the
canonicalization function, we are able to make large pretrained models
equivariant while maintaining their performance. This significantly improves
the robustness of these models to deterministic transformations of the data,
such as rotations. We believe this equivariant adaptation of large pretrained
models can help their domain-specific applications with known symmetry priors.Comment: 17 pages, 6 figures. Accepted to NeurIPS 202