IEEE Geoscience and Remote Sensing Magazine - December 2022 - 226

stores and updates the embeddings of samples with the
most recent ones at regular intervals. The memory bank
contains a feature representation mx
in dataset X. The representation mx
moving average of feature representations that were computed
in prior epochs. It enables replacing negative samples
mxby
their memory bank representations without
increasing the batch size. PIRL [74] and InstDisc [73] are
two representative methods that use a memory bank to
boost performance.
However, maintaining a memory bank during training
for each sample x
is an exponentially
can be a complicated task as:
1) It can be computationally expensive to update the representations
in the memory bank as the representations
get outdated quickly in a few passes.
2) The representations in the memory bank are always one
step behind the current encoding, which might bring
unnecessary mismatches.
To address these issues, the memory bank got replaced by a
separate module called the momentum encoder in MoCo [75].
Contrastive Loss
Contrastive Loss
Gradient
Gradient
Gradient
Gradient
q.kq.kq.k
qk qk qk
Encoder q
xq
(a)
Encoder k
xk
Encoder
xq
(b)
Sampling
Memory
Bank
Encoder
xq
(c)
FIGURE 11. A conceptual comparison of MoCo [75] with two previous contrastive mechanisms. Rather than end-to-end backpropagation
or sampling from a memory bank, MoCo encodes the new keys (key representation) on the fly by a momentum-updated encoder and maintains
a queue of keys (a dictionary) for storing negative samples. The size of the dictionary can be much larger than a typical minibatch size,
and the samples in the dictionary are progressively replaced. (a) End to end. (b) Memory bank. (c) MoCo.
Momentum
Encoder
xk
Contrastive Loss
(a)
(b)
(c)
(d)
(e)
(f)
(g)
(h)
(i)
(j)
FIGURE 12. Data augmentation operators studied in SimCLR [76]. The selected best group of data augmentations (random cropping, color jittering,
grayscaling, Gaussian blurring, and horizontal flipping) are widely referenced in the following self-supervised studies. (a) Original. (b) Crop
and resize. (c) Crop, resize, and flip. (d) Color drop. (e) Color jitter. (f) Rotate. (g) Cutout. (h) Gaussian noise. (i) Gaussian blur. (j) Sobel filter.
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