516 research outputs found
GOLLIC: Learning Global Context beyond Patches for Lossless High-Resolution Image Compression
Neural-network-based approaches recently emerged in the field of data
compression and have already led to significant progress in image compression,
especially in achieving a higher compression ratio. In the lossless image
compression scenario, however, existing methods often struggle to learn a
probability model of full-size high-resolution images due to the limitation of
the computation source. The current strategy is to crop high-resolution images
into multiple non-overlapping patches and process them independently. This
strategy ignores long-term dependencies beyond patches, thus limiting modeling
performance. To address this problem, we propose a hierarchical latent variable
model with a global context to capture the long-term dependencies of
high-resolution images. Besides the latent variable unique to each patch, we
introduce shared latent variables between patches to construct the global
context. The shared latent variables are extracted by a self-supervised
clustering module inside the model's encoder. This clustering module assigns
each patch the confidence that it belongs to any cluster. Later, shared latent
variables are learned according to latent variables of patches and their
confidence, which reflects the similarity of patches in the same cluster and
benefits the global context modeling. Experimental results show that our global
context model improves compression ratio compared to the engineered codecs and
deep learning models on three benchmark high-resolution image datasets, DIV2K,
CLIC.pro, and CLIC.mobile
An Introduction to Neural Data Compression
Neural compression is the application of neural networks and other machine
learning methods to data compression. Recent advances in statistical machine
learning have opened up new possibilities for data compression, allowing
compression algorithms to be learned end-to-end from data using powerful
generative models such as normalizing flows, variational autoencoders,
diffusion probabilistic models, and generative adversarial networks. The
present article aims to introduce this field of research to a broader machine
learning audience by reviewing the necessary background in information theory
(e.g., entropy coding, rate-distortion theory) and computer vision (e.g., image
quality assessment, perceptual metrics), and providing a curated guide through
the essential ideas and methods in the literature thus far
Backwards is the way forward: feedback in the cortical hierarchy predicts the expected future
Clark offers a powerful description of the brain as a prediction machine, which offers progress on two distinct levels. First, on an abstract conceptual level, it provides a unifying framework for perception, action, and cognition (including subdivisions such as attention, expectation, and imagination). Second, hierarchical prediction offers progress on a concrete descriptive level for testing and constraining conceptual elements and mechanisms of predictive coding models (estimation of predictions, prediction errors, and internal models)
Sparse representation based hyperspectral image compression and classification
Abstract
This thesis presents a research work on applying sparse representation to lossy hyperspectral image
compression and hyperspectral image classification. The proposed lossy hyperspectral image
compression framework introduces two types of dictionaries distinguished by the terms sparse
representation spectral dictionary (SRSD) and multi-scale spectral dictionary (MSSD), respectively.
The former is learnt in the spectral domain to exploit the spectral correlations, and the
latter in wavelet multi-scale spectral domain to exploit both spatial and spectral correlations in
hyperspectral images. To alleviate the computational demand of dictionary learning, either a
base dictionary trained offline or an update of the base dictionary is employed in the compression
framework. The proposed compression method is evaluated in terms of different objective
metrics, and compared to selected state-of-the-art hyperspectral image compression schemes, including
JPEG 2000. The numerical results demonstrate the effectiveness and competitiveness of
both SRSD and MSSD approaches.
For the proposed hyperspectral image classification method, we utilize the sparse coefficients
for training support vector machine (SVM) and k-nearest neighbour (kNN) classifiers. In particular,
the discriminative character of the sparse coefficients is enhanced by incorporating contextual
information using local mean filters. The classification performance is evaluated and compared
to a number of similar or representative methods. The results show that our approach could outperform
other approaches based on SVM or sparse representation.
This thesis makes the following contributions. It provides a relatively thorough investigation
of applying sparse representation to lossy hyperspectral image compression. Specifically,
it reveals the effectiveness of sparse representation for the exploitation of spectral correlations
in hyperspectral images. In addition, we have shown that the discriminative character of sparse
coefficients can lead to superior performance in hyperspectral image classification.EM201
Feature Reinforcement Learning: Part I: Unstructured MDPs
General-purpose, intelligent, learning agents cycle through sequences of
observations, actions, and rewards that are complex, uncertain, unknown, and
non-Markovian. On the other hand, reinforcement learning is well-developed for
small finite state Markov decision processes (MDPs). Up to now, extracting the
right state representations out of bare observations, that is, reducing the
general agent setup to the MDP framework, is an art that involves significant
effort by designers. The primary goal of this work is to automate the reduction
process and thereby significantly expand the scope of many existing
reinforcement learning algorithms and the agents that employ them. Before we
can think of mechanizing this search for suitable MDPs, we need a formal
objective criterion. The main contribution of this article is to develop such a
criterion. I also integrate the various parts into one learning algorithm.
Extensions to more realistic dynamic Bayesian networks are developed in Part
II. The role of POMDPs is also considered there.Comment: 24 LaTeX pages, 5 diagram
The Active Bayesian Brain and the Rorschach Task
The Rorschach offers a unique and interesting paradigm from the perspective of the (Bayesian) brain. This contribution to the cross-disciplinary special issue considers the Rorschach from the perspective of perceptual inference in the brain and how it might inform subject-specific differences in perceptual synthesis. Before doing so, we provide a broad overview of active inference in its various manifestations. In brief, active inference supposes that our perceptions are the best hypothesis to explain sensory impressions. On a Bayesian account, the requisite belief updating rests sensitively upon the precision or confidence ascribed to sensory input, relative to prior beliefs about the causes of sensations. This focus - on the balance between sensory and prior precision - has been a useful construct in both cognitive science (e.g., as a formal explanation for attention) and neuropsychology (e.g., as a formal explanation for aberrant or false inference in hallucinations). In this setting, false inference is generally understood as abnormally high precision afforded to high-level hypotheses or explanations for visual input, which may compensate for a failure to attenuate sensory precision. On this view, the Rorschach offers an interesting paradigm because the amount of precise information about the causes of visual input is deliberately minimized - and rendered ambiguous - thereby placing greater emphasis on prior beliefs entertained by the respondent. We close by exploring this issue and several other areas of intersection between Rorschach responding and active inference
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