101 research outputs found
Sample Mixed-Based Data Augmentation for Domestic Audio Tagging
Audio tagging has attracted increasing attention since last decade and has
various potential applications in many fields. The objective of audio tagging
is to predict the labels of an audio clip. Recently deep learning methods have
been applied to audio tagging and have achieved state-of-the-art performance,
which provides a poor generalization ability on new data. However due to the
limited size of audio tagging data such as DCASE data, the trained models tend
to result in overfitting of the network. Previous data augmentation methods
such as pitch shifting, time stretching and adding background noise do not show
much improvement in audio tagging. In this paper, we explore the sample mixed
data augmentation for the domestic audio tagging task, including mixup,
SamplePairing and extrapolation. We apply a convolutional recurrent neural
network (CRNN) with attention module with log-scaled mel spectrum as a baseline
system. In our experiments, we achieve an state-of-the-art of equal error rate
(EER) of 0.10 on DCASE 2016 task4 dataset with mixup approach, outperforming
the baseline system without data augmentation.Comment: submitted to the workshop of Detection and Classification of Acoustic
Scenes and Events 2018 (DCASE 2018), 19-20 November 2018, Surrey, U
Surfactant Induced Reservoir Wettability Alteration: Recent Theoretical and Experimental Advances in Enhanced Oil Recovery
Reservoir wettability plays an important role in various oil recovery processes. The origin and evolution of reservoir wettability were critically reviewed to better understand the complexity of wettability due to interactions in crude oil-brine-rock system, with introduction of different wetting states and their influence on fluid distribution in pore spaces. The effect of wettability on oil recovery of waterflooding was then summarized from past and recent research to emphasize the importance of wettability in oil displacement by brine. The mechanism of wettability alteration by different surfactants in both carbonate and sandstone reservoirs was analyzed, concerning their distinct surface chemistry, and different interaction patterns of surfactants with components on rock surface. Other concerns such as the combined effect of wettability alteration and interfacial tension (IFT) reduction on the imbibition process was also taken into account. Generally, surfactant induced wettability alteration for enhanced oil recovery is still in the stage of laboratory investigation. The successful application of this technique relies on a comprehensive survey of target reservoir conditions, and could be expected especially in low permeability fractured reservoirs and forced imbibition process
Trusted Multi-Scale Classification Framework for Whole Slide Image
Despite remarkable efforts been made, the classification of gigapixels
whole-slide image (WSI) is severely restrained from either the constrained
computing resources for the whole slides, or limited utilizing of the knowledge
from different scales. Moreover, most of the previous attempts lacked of the
ability of uncertainty estimation. Generally, the pathologists often jointly
analyze WSI from the different magnifications. If the pathologists are
uncertain by using single magnification, then they will change the
magnification repeatedly to discover various features of the tissues. Motivated
by the diagnose process of the pathologists, in this paper, we propose a
trusted multi-scale classification framework for the WSI. Leveraging the Vision
Transformer as the backbone for multi branches, our framework can jointly
classification modeling, estimating the uncertainty of each magnification of a
microscope and integrate the evidence from different magnification. Moreover,
to exploit discriminative patches from WSIs and reduce the requirement for
computation resources, we propose a novel patch selection schema using
attention rollout and non-maximum suppression. To empirically investigate the
effectiveness of our approach, empirical experiments are conducted on our WSI
classification tasks, using two benchmark databases. The obtained results
suggest that the trusted framework can significantly improve the WSI
classification performance compared with the state-of-the-art methods
Optimistic Model Rollouts for Pessimistic Offline Policy Optimization
Model-based offline reinforcement learning (RL) has made remarkable progress,
offering a promising avenue for improving generalization with synthetic model
rollouts. Existing works primarily focus on incorporating pessimism for policy
optimization, usually via constructing a Pessimistic Markov Decision Process
(P-MDP). However, the P-MDP discourages the policies from learning in
out-of-distribution (OOD) regions beyond the support of offline datasets, which
can under-utilize the generalization ability of dynamics models. In contrast,
we propose constructing an Optimistic MDP (O-MDP). We initially observed the
potential benefits of optimism brought by encouraging more OOD rollouts.
Motivated by this observation, we present ORPO, a simple yet effective
model-based offline RL framework. ORPO generates Optimistic model Rollouts for
Pessimistic offline policy Optimization. Specifically, we train an optimistic
rollout policy in the O-MDP to sample more OOD model rollouts. Then we relabel
the sampled state-action pairs with penalized rewards and optimize the output
policy in the P-MDP. Theoretically, we demonstrate that the performance of
policies trained with ORPO can be lower-bounded in linear MDPs. Experimental
results show that our framework significantly outperforms P-MDP baselines by a
margin of 30%, achieving state-of-the-art performance on the widely-used
benchmark. Moreover, ORPO exhibits notable advantages in problems that require
generalization
Dynamic Memory-based Curiosity: A Bootstrap Approach for Exploration
The sparsity of extrinsic rewards poses a serious challenge for reinforcement
learning (RL). Currently, many efforts have been made on curiosity which can
provide a representative intrinsic reward for effective exploration. However,
the challenge is still far from being solved. In this paper, we present a novel
curiosity for RL, named DyMeCu, which stands for Dynamic Memory-based
Curiosity. Inspired by human curiosity and information theory, DyMeCu consists
of a dynamic memory and dual online learners. The curiosity arouses if
memorized information can not deal with the current state, and the information
gap between dual learners can be formulated as the intrinsic reward for agents,
and then such state information can be consolidated into the dynamic memory.
Compared with previous curiosity methods, DyMeCu can better mimic human
curiosity with dynamic memory, and the memory module can be dynamically grown
based on a bootstrap paradigm with dual learners. On multiple benchmarks
including DeepMind Control Suite and Atari Suite, large-scale empirical
experiments are conducted and the results demonstrate that DyMeCu outperforms
competitive curiosity-based methods with or without extrinsic rewards. We will
release the code to enhance reproducibility
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