【ML】ICLR2016_Delving Deeper into Convolutional Networks

ICLR2016_DELVING DEEPER INTO CONVOLUTIONAL NETWORKS

Note here: Ballas recently proposed a novel framework on learning video representation, following is the review note after reading his paper.

Link: http://arxiv.org/pdf/1511.06432v4.pdf

[Brief introduction to some neural networks]

CNN: excellent in static image classification

RNN: can understand temporal sequences in various learning tasks
(however, with exploding or vanishing weights problem)
---> LSTM/GRU are proposed to avoid this problem

RCN: leverage properties from both CNN and RNN, use CNN top level feature map as input of RNN, it has recently introduced to learn video representations.

[Video reprensentation]

Mmotivation:
Adopt RCN as basic model.
- Top-level feature map presents high sementic features, namely the spatial naunces are ignored after pooling.
- However, frame-to-frame temporal variation is known to be smooth, which is the key for action recognition from videos.
(we need a new model to adapt this problem)

[Proposed models]

GRU-RCN:
- replace recurrent units in RCN with GRU.

(z: activation gate, decides to what degree previous hidden state would contribute to the next hidden state)
(r: reset gate, decides whether or not last hidden state should be propagated into next state)
(~h: candidate hidden state, it'll pass through the activatin gate)
(h: final hidden state)

Problems:
- number of parameters in fully-connected layer is huge due to size of conv map.
- fully-connected layers break the spatial structure of conv map.

Trick:
- replace the fully-connected units in GRU with convolution operations, which can keep spatial structure and reduce number of parameters meanwhile.

Intuition:
- we can see the propagation of hidden states as a process of convolution.
if so, the next hidden state percepts spatial structure of all the previous states. as the sequence goes further, the receptive field on previous states are larger, and we only get a general concept of frames in the beginning.
- compare to our cognition system, it does make sense!

Stacked GRU-RCN:
- it applies L GRU-RCNs independently on each convolutional map.
- tile up L GRU-RCNs.
- feed L final time-step hidden states into a classifier.