【Writing】 Expressions for paper writing

14 Apr 2022 in Self Study

• Expressions for paper writing

Expressions

• Apart from the aforementioned forms/works
• be aims to = be targeted to
• no need to reinvent general segmentation architectures
• the following observation explains the superiority of N.
• In turn, = Eventually
• adopt A with the following changes.
• it seems natural to
• work well and produce even better results than
• F is a network parameterized by θ
• To have a more thorough comparison,
• hinders the applicability of segmentation models.
• Therefore, instead of improving ~, our work focused on
• leverages the availability of extra unlabeled or weakly annotated
• , with the aim of narrowing the gap to the supervised models
• we specify = demonstrate = present = indicate

• and vice versa.

  but not vice versa.

• It is expected that -.
• As illustrated in Fig,
• We investigate how
• invariance between the outputs of two identical networks fed with distorted versions of a sample.
• A be encouraged to be B. (<- we make A to B.)
• in lieu of = instead of
• This line of methods = Previous methods
• When it comes to ~ there is ~. = In terms of = In the sense of = In the context of
• With this in mind, we propose = According to this motivation,
• From the other end, = In contrast, = Conversely
• can be converted into = considered as = represent as =
• Our research directions can be classified into three areas:
• The onus of generalization lies heavily on the data augmentation pipeline
• we propose the novel loss that can be succinctly written as a contrastive learning objective
• The effect of hard negatives has so far been neglected.
• We delve deeper into

• tend to, be prone to, be likely to

  they are prone to overlook spatial consistency

• A conditional normalization layer that modulates (norm+denorm) the activations.
• The conditional normalization layer can effectively propagate semantic information.
• In line with the other datasets in Wilds, we evaluate using a classification task.
• In general, we believe that these results lend support to the conclusion that ~ (Interpretation of experimental results / additional insight).
• With the intuition to V, this paper propose
• InfoNCE loss used a batch of negative samples, which is found to be significant for performance boost.
• Concurrent to [20],
• the same loss has also been proposed in [25] with the motivation to perform
• [25] uses a memory bank to store representations of negative samples, which allows a large negative sample size.
• we deviate from recent works, and advocate a two-step approach w
• this pulling-near process is accomplished via label supervision
• we encourage the predicted representations of augmented data points to be close
• However, the ideal unbiased objective is unachievable in practice since ~. This dilemma poses the question whether
• The key idea underlying our approach is to indirectly approximate
• the power of contrastive learning has yet to be fully unleashed, as current methods are trained only on instance-level pretext tasks, leading to representations that may be sub-optimal for downstream tasks
• Future o2020densevisual leverage pixel correspondences derived from the view generation process. / The advantages are derived from (1) method1 (2) method2
• suggest = propose = design
• Use = utilze = leverage = exploit = advocate = adapt
• we advocate masking of highly-attended patches, in a sense the opposite of MST,
• This does not adversely affect the practicality of TTA, because restoring individual source data from the source statistics is quite difficult.
• we draw inspiration from recent literature
• Finally, we shed light on the strong potential of TTT through a theoretical analysis
• Note that our goal is to explore ~. Therefore, we do not necessitate any ~.
• SimMIM adopts both unmasked and masked patch as the input of encoder, which might increase the compute on the encoder in a wasting manner.
• On the downside, calculating higher-order update directions is computationally more expensive than ¦rstorder updates. The operation uses more memory for storing statistics and involves matrix inversion, thus hindering the applicability of higher-order optimizers in practice.
• Generally, each layer in a neural network applies a linear transformation on its inputs, followed by a non-linear activation function.
• The combined local updates look rather like a higher-order update. Empirically, we show that LocoProp outperforms ¦rst-order methods on a deep autoencoder benchmark and performs comparably to higher-order optimizers.
• An early attempt to combine SVM with DL was made in [28], which however has a different motivation with ours and only studies the output layer with some preliminary experimental results.
• we introduce the method used to detect OOD samples at inference time.
• we discover some irrationality on OOD splitting.
• The above dataset-dependent OOD (DD-OOD) benchmarks may indicate that models are attempting to overfit the low-level discrepancy on the negligible covariate shifts between data sources while ignoring inherent semantics.