[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 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.