Tosupportthis, we compute the cosine similarity between representations from augmented and original samples, i.e., CS = Ex D,t T[sim(g f(t(x)),g f(x))]. For linear evaluation benchmarks, we randomly choose validation samples in the training split for each dataset when the validation split is not officially provided. Note that the pretraining setups are the same as they officiallyusedforImageNet pretraining described in[2,5,30]. When incorporating our AugSelf into the methods, we use λ=1.0andAAugSelf ={crop,color},unlessotherwisestated. Other hyperparameters are the same as the ImageNet100 setup describedinSectionF.1.

Furthermore, AugSelf can easily be incorporated into recent state-of-the-art representation learning methods with a negligible additional training cost.

The most well-known work in the reward shaping domain is the potential-based reward shaping (PBRS) method [12], which is the first to show that policy invariance can be guaranteed if the shaping reward function is in the form of the difference of potential values.

Individuals inthe data set were selected atrandom from hospital records.

Individuals inthe data set were selected atrandom from hospital records.

This inevitably introduces a tremendous memory andcompute overheadintotheLLMs, whichisparticularly significant when it comes to high-resolution images and multi-frame videos. Several previous works attempt to mitigate this issue by proposing various token compression strategies. A straightforward way is to reduce the number of tokens with spatial grouping [70, 47]. Instead of pooling vision tokens, a few work instead to concatenate local tokens along the feature dimension to preserve visual information [11, 48]. Moreover, other works seek more sophisticated token resampling, such as Q-Former [43], Perceiver [4]and Abstractor [8],etc.