- Jan 2021
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arxiv.org arxiv.org
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[21, 39] directlyuse conventional CNN or deep belief networks (DBN)
interesting, read!
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f⌧+n<T,then:GG+ nV(S⌧+n
V(terminal state) = 0
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- Dec 2020
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↵Gtr⇡(At|St,✓t)⇡(At|St,✓t)
notice that the multiplier of the gradient here: G_t / pi(a|s) is positive, meaning we are always going in the same direction as the gradient. using a baseline G_t - v(S_t) allows us to revers this direction if G_t is lower than the baseline
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Actor–Critic with Eligibility Traces (continuing), for estimating⇡✓⇡⇡⇤
actor critic algorithm one step TD:
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(13.16)
very similar to box 199 but without h(s)
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✓✓+↵✓I rln⇡(A|S,✓)
actor-critic with state value baseline update, with discounting!
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ww+↵w rˆv(S,w)
TD(0) update
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Gt:t+1 ˆv(St,w)
same as REINFORCE MC baseline, but with the sampled G replaced with a bootstrapped G
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That is,wis a single component,w.
constant baseline?
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If there is discounting ( <1) itshould be treated as a form of termination, which can be done simply by includinga factor of in the second term of
termination because discounting by gamma is equivalent to a non-disounted case, but with termination probability gamma
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- Oct 2020
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Local file Local file
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Chenget al.[92] design a multi-channel parts-aggregated deep convolutional network byintegrating the local body part features and the global full-body features in a triplet training framework
TODO: read this and find out what the philosophy behind parts-based model is??
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adaptive average pooling
what is this?
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Generation/Augmentation
TODO: read
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Using theannotated source data in the training process of the targetdomain is beneficial for cross-dataset learning
What? Clarify
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Dy-namic graph matching (DGM)
super interesting, but hardly applicable. do rad though!
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Sample Rate Learning
what
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Singular VectorDecomposition (SVDNet)
seems interesting, "iteratively integrate the orthogonality constraint in CNN training"
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Omni-Scale Network (OSNet)
read paper again to see if any good ideas for architecture
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bottleneck laye
Bottleneck layers do a 1x1 convolution to reduce the dimensionality, before a 3x3 convolution, to save computation
https://medium.com/@erikgaas/resnet-torchvision-bottlenecks-and-layers-not-as-they-seem-145620f93096
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Global Feature Representation Learning
someething that came up whilst looking through papers in attention: https://arxiv.org/pdf/1709.01507.pdf squeeze-and-excitation
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[68]
Parts-based paper, interesting approach
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