【Writing】 Expressions for paper writing

  • 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 tothey 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.

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