96 research outputs found
Beyond Monte Carlo Tree Search: Playing Go with Deep Alternative Neural Network and Long-Term Evaluation
Monte Carlo tree search (MCTS) is extremely popular in computer Go which
determines each action by enormous simulations in a broad and deep search tree.
However, human experts select most actions by pattern analysis and careful
evaluation rather than brute search of millions of future nteractions. In this
paper, we propose a computer Go system that follows experts way of thinking and
playing. Our system consists of two parts. The first part is a novel deep
alternative neural network (DANN) used to generate candidates of next move.
Compared with existing deep convolutional neural network (DCNN), DANN inserts
recurrent layer after each convolutional layer and stacks them in an
alternative manner. We show such setting can preserve more contexts of local
features and its evolutions which are beneficial for move prediction. The
second part is a long-term evaluation (LTE) module used to provide a reliable
evaluation of candidates rather than a single probability from move predictor.
This is consistent with human experts nature of playing since they can foresee
tens of steps to give an accurate estimation of candidates. In our system, for
each candidate, LTE calculates a cumulative reward after several future
interactions when local variations are settled. Combining criteria from the two
parts, our system determines the optimal choice of next move. For more
comprehensive experiments, we introduce a new professional Go dataset (PGD),
consisting of 253233 professional records. Experiments on GoGoD and PGD
datasets show the DANN can substantially improve performance of move prediction
over pure DCNN. When combining LTE, our system outperforms most relevant
approaches and open engines based on MCTS.Comment: AAAI 201
MLIC++: Linear Complexity Multi-Reference Entropy Modeling for Learned Image Compression
Recently, multi-reference entropy model has been proposed, which captures
channel-wise, local spatial, and global spatial correlations. Previous works
adopt attention for global correlation capturing, however, the quadratic
cpmplexity limits the potential of high-resolution image coding. In this paper,
we propose the linear complexity global correlations capturing, via the
decomposition of softmax operation. Based on it, we propose the MLIC, a
learned image compression with linear complexity for multi-reference entropy
modeling. Our MLIC is more efficient and it reduces BD-rate by 12.44% on
the Kodak dataset compared to VTM-17.0 when measured in PSNR. Code will be
available at https://github.com/JiangWeibeta/MLIC.Comment: Accepted at ICML 2023 Neural Compression Workshop. Extension work of
our ACMMM 2023 paper MLIC: Multi-Reference Entropy Model for Learned Image
Compressio
Deciding Fast and Slow in Risk Decision Making: An Experimental Study
The current study presents findings of an experiment. Response time was used to investigate fast decidersā (FD) and slow decidersā (SD) behavioral differences. SDs were found to be more cognitive than FDs and this could induce an increase in average response time. Both FDs and SDs showed aversion to extreme options, but they behaved differently with option āSā being āsaferā among groups. Moreover, FDs responded more instinctively to negative feedbacks
SLIC: Self-Conditioned Adaptive Transform with Large-Scale Receptive Fields for Learned Image Compression
Learned image compression has achieved remarkable performance. Transform,
plays an important role in boosting the RD performance. Analysis transform
converts the input image to a compact latent representation. The more compact
the latent representation is, the fewer bits we need to compress it. When
designing better transform, some previous works adopt Swin-Transformer. The
success of the Swin-Transformer in image compression can be attributed to the
dynamic weights and large receptive field.However,the LayerNorm adopted in
transformers is not suitable for image compression.We find CNN-based modules
can also be dynamic and have large receptive-fields. The CNN-based modules can
also work with GDN/IGDN. To make the CNN-based modules dynamic, we generate the
weights of kernels conditioned on the input feature. We scale up the size of
each kernel for larger receptive fields. To reduce complexity, we make the
CNN-module channel-wise connected. We call this module Dynamic Depth-wise
convolution. We replace the self-attention module with the proposed Dynamic
Depth-wise convolution, replace the embedding layer with a depth-wise residual
bottleneck for non-linearity and replace the FFN layer with an inverted
residual bottleneck for more interactions in the spatial domain. The
interactions among channels of dynamic depth-wise convolution are limited. We
design the other block, which replaces the dynamic depth-wise convolution with
channel attention. We equip the proposed modules in the analysis and synthesis
transform and receive a more compact latent representation and propose the
learned image compression model SLIC, meaning Self-Conditioned Adaptive
Transform with Large-Scale Receptive Fields for Learned Image Compression
Learned Image Compression. Thanks to the proposed transform modules, our
proposed SLIC achieves 6.35% BD-rate reduction over VVC when measured in PSNR
on Kodak dataset.Comment: Submitted to TCSV
MLIC: Multi-Reference Entropy Model for Learned Image Compression
Recently, learned image compression has achieved remarkable performance. The
entropy model, which estimates the distribution of the latent representation,
plays a crucial role in boosting rate-distortion performance. However, most
entropy models only capture correlations in one dimension, while the latent
representation contain channel-wise, local spatial, and global spatial
correlations. To tackle this issue, we propose the Multi-Reference Entropy
Model (MEM) and the advanced version, MEM. These models capture the
different types of correlations present in latent representation. Specifically,
We first divide the latent representation into slices. When decoding the
current slice, we use previously decoded slices as context and employ the
attention map of the previously decoded slice to predict global correlations in
the current slice. To capture local contexts, we introduce two enhanced
checkerboard context capturing techniques that avoids performance degradation.
Based on MEM and MEM, we propose image compression models MLIC and
MLIC. Extensive experimental evaluations demonstrate that our MLIC and
MLIC+ models achieve state-of-the-art performance, reducing BD-rate by
and on the Kodak dataset compared to VTM-17.0 when measured in PSNR.Comment: Fixed some typos and re-organized the pape
A No-Reference Blocking Artifacts Metric Using Selective Gradient and Plainness Measures
Abstract. This paper presents a novel no-reference blocking artifacts metric us-ing selective gradient and plainness (BAM_SGP) measures for DCT-coded images. A boundary selection criterion is introduced to distinguish the blocking artifacts boundaries from the true-edge boundaries, which ensures that the most potential artifacts boundaries are involved in the measurement. Next, the arti-facts are evaluated by the gradient and plainness measures indicating different aspects of blocking artifacts characteristics. Then these two measures are fused into a metric of blocking artifacts. Compared with some existing metrics, ex-periments on the LIVE database and our own test set show that the proposed metric can keep better consistent with Mean Opinion Score (MOS)
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